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市場調查報告書
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2120910

人工智慧驅動的食品建議市場預測至2034年：按建議類型、技術方法、部署模式、應用領域、最終用戶和地區分類的全球分析

AI-Based Food Recommendation Market Forecasts to 2034 - Global Analysis By Recommendation Type, Technology Approach, Deployment Mode, Application Context, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球人工智慧驅動的膳食提案市場預計將在 2026 年達到 183 億美元，並在預測期內以 37.0% 的複合年成長率成長，到 2034 年達到 2,276 億美元。

人工智慧驅動的膳食提案平台透過分析個人偏好、健康需求和情境數據，利用數位化管道提供個人化的膳食選擇。這些解決方案運用機器學習、自然語言處理 (NLP) 和行為分析等先進技術，識別使用者的飲食習慣、食物不耐受、文化偏好和健康目標。它們還結合行動應用、穿戴式裝置和交易記錄等數據，進一步提升推薦的準確性和效用。餐廳、線上食品經銷商、健身應用程式和其他企業正在部署這些系統，以增強客戶參與、最佳化服務並幫助用戶做出更健康的選擇。在日益成長的個人需求驅動下，人工智慧解決方案正在重塑世界各地人們在日常生活中尋找、選擇和體驗食物的方式。

根據 MDPI 學術期刊《營養素》上發表的研究，人工智慧驅動的食物識別應用程式的準確率高達 97%，證明了人工智慧技術在分析膳食攝取量和提供個人化建議方面的有效性。

對個人化營養的需求日益成長

消費者健康意識的不斷提高推動了對個人化營養的需求，進而加速了人工智慧驅動的飲食建議的普及。人們越來越傾向於選擇能夠滿足自身醫療需求、健身目標、過敏史和日常生活習慣的飲食方案。先進的人工智慧工具能夠處理大量用戶數據，包括飲食模式和健康指標，從而產生精準的提案。這種個人化的方法不僅提升了使用者體驗，也有助於養成更健康的飲食習慣。隨著人們對預防醫學和健康管理的日益重視，對精準營養指導的需求也不斷成長。因此，人工智慧驅動的建議系統正成為世界各地人們做出明智飲食選擇、維持健康生活方式的關鍵工具。

對資料隱私和安全的擔憂

用戶對資料保護日益成長的擔憂阻礙了人工智慧膳食推薦系統的普及。這些解決方案依賴於收集個人資訊，例如飲食習慣、醫療資訊和生活方式選擇，因此存在隱私風險。資料外洩、網路攻擊和濫用等潛在威脅使用戶不願共用敏感資訊。此外，嚴格的資料安全法規也為企業增加了額外的合規負擔。使用者對企業如何處理個人資料缺乏信任可能會阻礙這項技術的廣泛應用。隨著人們對數位隱私意識的提高，企業必須投入大量資源來確保安全，這可能會限制創新並減緩整體市場成長。

與健康和保健生態系統的融合

將人工智慧驅動的膳食推薦平台與更廣泛的數位健康生態系統融合，蘊藏著巨大的成長潛力。透過與健身追蹤器、健康應用程式和遠端醫療服務整合，這些系統能夠基於即時健康資訊提供個人化的營養建議。這種根據個人健康目標和醫療需求量身定做的方法，能夠提升使用者體驗，並促進更健康的生活方式選擇。預防醫學和整體健康概念的轉變，進一步推動了這一趨勢的普及。隨著數位健康技術在消費者中日益普及，對智慧化、個人化飲食解決方案的需求也不斷成長。這一趨勢正為全球人工智慧驅動的膳食推薦服務的創新和發展開闢新的道路。

對第三方平台和資料來源的依賴

過度依賴外部平台和第三方資料來源會為人工智慧驅動的膳食推薦系統帶來重大風險。這些平台通常依賴膳食管理應用、連網型設備和外部服務供應商的數據，從而限制了對數據可靠性的直接控制。 API存取、監管政策或資料共用協議的變更都可能對系統效能產生負面影響。對外部合作夥伴的依賴也可能帶來不確定性和營運挑戰。任何資料可用性的中斷都可能對建議品質和使用者滿意度產生負面影響。這種外部依賴增加了系統的脆弱性，而在競爭激烈的數位化環境中，企業必須不斷適應自身無法直接控制的因素。

新型冠狀病毒（COVID-19）的影響：

疫情對人工智慧食品建議平台的發展起到了至關重要的作用，因為人們在食品和健康決策方面越來越依賴數位化解決方案。旅行限制和安全擔憂導致線上食品服務的使用量激增，為人工智慧系統提供了大量數據，使其能夠不斷最佳化個人化推薦。人們對健康和免疫力的日益關注也推動了對客製化營養指導的需求。企業利用人工智慧來改善用戶互動和服務效率。儘管物流受到暫時性干擾，消費者偏好也發生了變化，但總體影響是積極的，加速了數位化進程，並進一步鞏固了人工智慧在塑造全球現代飲食習慣方面的重要作用。

在預測期內，個人化飲食計劃細分市場預計將佔據最大的市場佔有率。

預計在預測期內，個人化飲食計畫細分市場將佔據最大的市場佔有率，因為它能有效滿足個人的飲食需求、習慣和健康目標。使用者更傾向於選擇根據自身生活方式、營養需求和個人目標量身定做的飲食計劃。人工智慧技術能夠處理食物偏好、卡路里攝取量和生活方式模式等數據，從而產生個人化的每日飲食提案。這不僅簡化了決策過程，還有助於改善營養均衡並提高時間效率。因此，個人化飲食計畫在全球數位健康工具、健身應用程式和現代食品服務平台中持續廣泛應用。

在預測期內，食品零售商和電子商務平台領域預計將實現最高的複合年成長率。

在預測期內，受消費者對電商通路日益成長的依賴推動，食品零售商和電商平台預計將呈現最高的成長率。這些平台利用人工智慧分析用戶行為、購買歷史和偏好，從而提供個人化提案。這不僅提升了購物便利性，增強了用戶參與度，也促進了消費成長。快速配送模式的興起和數位基礎設施的擴展進一步推動了這一趨勢。各公司不斷部署複雜的建議系統，以保持競爭力並滿足不斷變化的消費者期望。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這得益於其成熟的技術生態系統、人工智慧解決方案的廣泛應用以及在該行業中的強大影響力。該地區的消費者積極使用外送應用、健身工具和連網型設備等數位平台，從而為個人化服務提供了豐富的數據。人們對健康生活方式的日益重視以及消費者對便利性的日益追求，正在推動對客製化建議的需求。這種有利的環境正在促進人工智慧解決方案的快速普及，使北美成為全球食品建議市場成長的關鍵區域。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於數位技術的積極應用以及行動和線上平台使用量的增加。智慧型手機和網路服務的普及促使消費者積極使用外送應用、線上雜貨服務和健康平台。該地區生活方式的轉變以及對個人化體驗日益成長的需求，推動了對智慧建議系統的需求。政府對數位化發展的支持進一步加速了這一成長，使亞太地區成為人工智慧食品建議技術快速發展的關鍵區域。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
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    • 對主要公司進行SWOT分析（最多3家公司）
  • 區域細分
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    • 根據產品系列、地理覆蓋範圍和策略聯盟對領先公司進行基準分析。

目錄

第1章：執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章：全球人工智慧驅動的食品建議市場：按建議類型分類

  • 個人化膳食計劃
  • 營養指導和膳食最佳化
  • 口味和喜好偏好
  • 關於餐廳和餐飲服務的提案
  • 推薦的食品雜貨和食材

第6章：全球人工智慧驅動型食品建議市場：依技術方法分類

  • 機器學習模型
  • 自然語言處理（NLP）
  • 電腦視覺
  • 混合人工智慧系統

第7章：全球人工智慧驅動的食品建議市場：依部署模式分類

  • 基於雲端的平台
  • 本地部署解決方案
  • 行動應用
  • 嵌入式系統

第8章：全球人工智慧驅動的食品建議市場：按應用場景分類

  • 健康與保健
  • 生活方式和便利
  • 永續性
  • 適應不同文化和地區的飲食

第9章：全球人工智慧驅動的食品建議市場：按最終用戶分類

  • 個人消費者
  • 餐廳及餐飲服務供應商
  • 食品零售商與電子商務平台
  • 營養師和醫療保健專業人員
  • 食品製造商

第10章：全球人工智慧驅動的食品建議市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第11章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第12章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第13章：公司簡介

  • Mealzo
  • Newseum Lab
  • OttoChef AI
  • Dave's List
  • SaladStop!
  • Food for Health
  • Little Lunches
  • GrubTok
  • Spoon Guru
  • Tyana.app
  • Calo
  • Forki
  • HelloFresh
  • Factor
  • Home Chef
  • Sunbasket
  • CookUnity
  • Green Chef
Product Code: SMRC39093

According to Stratistics MRC, the Global AI-Based Food Recommendation Market is accounted for $18.3 billion in 2026 and is expected to reach $227.6 billion by 2034 growing at a CAGR of 37.0% during the forecast period. AI-powered food recommendation platforms evaluate individual tastes, health requirements, and situational data to provide tailored meal options through digital channels. Using advanced technologies like machine learning, NLP, and behavioral analysis, these solutions identify eating habits, sensitivities, cultural preferences, and wellness objectives. They combine insights from mobile apps, wearable devices, and transaction records to increase precision and usefulness. Businesses such as restaurants, online grocers, and fitness apps adopt these systems to boost engagement, refine offerings, and encourage better dietary decisions. Growing demand for customization is driving AI-enabled solutions to reshape how people explore, choose, and experience food in daily routines globally.

According to findings in MDPI Nutrients Journal, AI-enabled food recognition apps achieved accuracy levels up to 97%, demonstrating the effectiveness of AI technologies in analyzing food intake and supporting personalized recommendations.

Market Dynamics:

Driver:

Growing demand for personalized nutrition

Growing health consciousness among consumers is encouraging the demand for individualized nutrition, thereby boosting AI-based food recommendation adoption. People increasingly favor meal options tailored to their medical needs, fitness objectives, allergies, and daily routines. Advanced AI tools process extensive user data, such as eating patterns and health indicators, to generate accurate suggestions. This personalized approach improves user experience and supports better dietary habits. With the rising focus on preventive healthcare and wellness, the need for precise nutritional guidance is accelerating. Consequently, AI-enabled recommendation systems are becoming vital in helping individuals make informed food choices and maintain healthier lifestyles worldwide.

Restraint:

Data privacy and security concerns

Rising concerns about user data protection are restricting the adoption of AI-based food recommendation systems. These solutions depend on gathering personal details such as eating patterns, medical information, and lifestyle choices, which raises privacy risks. Potential threats like data leaks, cyberattacks, and unauthorized usage discourage users from sharing sensitive information. Furthermore, stringent regulations around data security create additional compliance burdens for businesses. Lack of trust in how companies handle personal data can hinder widespread acceptance. As digital privacy awareness increases, organizations must allocate significant resources to ensure security, which may limit innovation and slow the overall growth of the market.

Opportunity:

Integration with health and wellness ecosystems

The integration of AI-driven food recommendation platforms with broader digital health ecosystems offers strong growth potential. By linking with fitness trackers, wellness applications, and telemedicine services, these systems can provide customized nutrition advice using real-time health insights. This alignment with personal health goals and medical needs enhances user experience and promotes better lifestyle choices. The shift toward preventive care and holistic well-being further supports adoption. As digital health technologies gain traction among consumers, the need for smart, personalized dietary solutions continues to increase. This trend opens new avenues for innovation and expansion in AI-powered food recommendation services worldwide.

Threat:

Dependence on third-party platforms and data sources

Heavy reliance on external platforms and third-party data sources poses a significant risk to AI-driven food recommendation systems. These platforms often depend on data from food apps, connected devices, and external service providers, reducing direct control over data reliability. Modifications in API access, regulatory policies, or data-sharing agreements can negatively impact system performance. Dependence on external partners may also introduce uncertainties and operational challenges. Any disruption in data availability can lower recommendation quality and user satisfaction. This external reliance increases vulnerability, requiring companies to continuously adjust to factors outside their direct influence in the competitive digital landscape.

Covid-19 Impact:

The pandemic played a crucial role in boosting the growth of AI-based food recommendation platforms as people increasingly depended on digital solutions for meals and health-related decisions. Movement restrictions and safety concerns led to higher usage of online food services, providing extensive data for AI systems to refine personalization. Growing focus on health and immunity also drove demand for customized nutrition guidance. Companies leveraged AI to improve user interaction and service efficiency. Despite temporary disruptions in logistics and shifting preferences, the overall impact was positive, accelerating digital adoption and reinforcing AI's importance in shaping modern food consumption habits worldwide.

The personalized meal planning segment is expected to be the largest during the forecast period

The personalized meal planning segment is expected to account for the largest market share during the forecast period as it effectively addresses individual dietary needs, habits, and health aspirations. Users prefer meal plans designed specifically for their routines, nutritional requirements, and personal goals. AI technologies process data like food preferences, calorie intake, and lifestyle patterns to generate tailored daily meal suggestions. This not only simplifies decision-making but also promotes better nutrition and time efficiency. As a result, personalized meal planning continues to gain traction across digital health tools, fitness applications, and modern food service platforms worldwide.

The grocery retailers & e-commerce platforms segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the grocery retailers & e-commerce platforms segment is predicted to witness the highest growth rate, driven by increasing consumer reliance on e-commerce channels. These platforms use artificial intelligence to interpret user behaviour, purchase history, and preferences to offer personalized suggestions. This improves shopping convenience, boosts engagement, and encourages higher spending. The rise of quick delivery models and expanding digital infrastructure further supports this trend. Companies are continuously adopting advanced recommendation systems to remain competitive and meet evolving consumer expectations.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, owing to its well-established technological ecosystem, widespread use of AI solutions, and strong industry presence. The region's consumers actively utilize digital platforms such as food delivery apps, fitness tools, and connected devices, providing rich data for personalized services. Increasing awareness of healthy lifestyles and convenience-based consumption boosts demand for customized recommendations. This favourable environment supports the rapid adoption of AI-powered solutions, positioning North America as a key contributor to the global growth of the food recommendation market.

Region with highest CAGR:

Over the forecast period, the Asia-Pacific region is anticipated to exhibit the highest CAGR, driven by strong digital adoption and growing use of mobile and online platforms. Increasing access to smart phones and internet services has enabled consumers to actively engage with food delivery apps, online grocery services, and wellness platforms. The region's evolving lifestyles and rising demand for personalized experiences are boosting the need for intelligent recommendation systems. Government support for digital development further accelerates growth, making Asia-Pacific a key region for the rapid advancement of AI-powered food recommendation technologies.

Key players in the market

Some of the key players in AI-Based Food Recommendation Market include Mealzo, Newseum Lab, OttoChef AI, Dave's List, SaladStop!, Food for Health, Little Lunches, GrubTok, Spoon Guru, Tyana.app, Calo, Forki, HelloFresh, Factor, Home Chef, Sunbasket, CookUnity and Green Chef.

Key Developments:

In March 2026, Little Lunches announced the launch of its AI Dietitian Assistant - a secure, multilingual platform designed to extend expert-led nutrition guidance to families instantly and at scale. The AI Dietitian Assistant transforms the clinical expertise of Little Lunches' certified dietitians, pediatricians, and feeding therapists into a real-time, conversational experience.

In March 2025, Grubtech and Wobot.ai have announced a strategic partnership. The partnership addresses modernization needs in the hospitality sector. Grubtech's platform integrates with food aggregators, POS systems, and logistics providers to digitize order workflows and improve operational visibility. This integration reportedly reduces costs and accelerates preparation and delivery timelines.

Recommendation Types Covered:

  • Personalized Meal Planning
  • Nutritional Guidance & Diet Optimization
  • Flavor & Taste Preference Matching
  • Restaurant & Food Service Suggestions
  • Grocery & Ingredient Recommendations

Technology Approaches Covered:

  • Machine Learning Models
  • Natural Language Processing (NLP)
  • Computer Vision
  • Hybrid AI Systems

Deployment Modes Covered:

  • Cloud-Based Platforms
  • On-Premise Solutions
  • Mobile Applications
  • Embedded Systems

Application Contexts Covered:

  • Health & Wellness
  • Lifestyle & Convenience
  • Sustainability
  • Cultural & Regional Cuisine Adaptation

End Users Covered:

  • Individual Consumers
  • Restaurants & Food Service Providers
  • Grocery Retailers & E-Commerce Platforms
  • Nutritionists & Healthcare Providers
  • Food Manufacturers

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI-Based Food Recommendation Market, By Recommendation Type

  • 5.1 Personalized Meal Planning
  • 5.2 Nutritional Guidance & Diet Optimization
  • 5.3 Flavor & Taste Preference Matching
  • 5.4 Restaurant & Food Service Suggestions
  • 5.5 Grocery & Ingredient Recommendations

6 Global AI-Based Food Recommendation Market, By Technology Approach

  • 6.1 Machine Learning Models
  • 6.2 Natural Language Processing (NLP)
  • 6.3 Computer Vision
  • 6.4 Hybrid AI Systems

7 Global AI-Based Food Recommendation Market, By Deployment Mode

  • 7.1 Cloud-Based Platforms
  • 7.2 On-Premise Solutions
  • 7.3 Mobile Applications
  • 7.4 Embedded Systems

8 Global AI-Based Food Recommendation Market, By Application Context

  • 8.1 Health & Wellness
  • 8.2 Lifestyle & Convenience
  • 8.3 Sustainability
  • 8.4 Cultural & Regional Cuisine Adaptation

9 Global AI-Based Food Recommendation Market, By End User

  • 9.1 Individual Consumers
  • 9.2 Restaurants & Food Service Providers
  • 9.3 Grocery Retailers & E-Commerce Platforms
  • 9.4 Nutritionists & Healthcare Providers
  • 9.5 Food Manufacturers

10 Global AI-Based Food Recommendation Market, By Geography

  • 10.1 North America
    • 10.1.1 United States
    • 10.1.2 Canada
    • 10.1.3 Mexico
  • 10.2 Europe
    • 10.2.1 United Kingdom
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Netherlands
    • 10.2.7 Belgium
    • 10.2.8 Sweden
    • 10.2.9 Switzerland
    • 10.2.10 Poland
    • 10.2.11 Rest of Europe
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 Japan
    • 10.3.3 India
    • 10.3.4 South Korea
    • 10.3.5 Australia
    • 10.3.6 Indonesia
    • 10.3.7 Thailand
    • 10.3.8 Malaysia
    • 10.3.9 Singapore
    • 10.3.10 Vietnam
    • 10.3.11 Rest of Asia Pacific
  • 10.4 South America
    • 10.4.1 Brazil
    • 10.4.2 Argentina
    • 10.4.3 Colombia
    • 10.4.4 Chile
    • 10.4.5 Peru
    • 10.4.6 Rest of South America
  • 10.5 Rest of the World (RoW)
    • 10.5.1 Middle East
      • 10.5.1.1 Saudi Arabia
      • 10.5.1.2 United Arab Emirates
      • 10.5.1.3 Qatar
      • 10.5.1.4 Israel
      • 10.5.1.5 Rest of Middle East
    • 10.5.2 Africa
      • 10.5.2.1 South Africa
      • 10.5.2.2 Egypt
      • 10.5.2.3 Morocco
      • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

  • 11.1 Industry Value Network and Supply Chain Assessment
  • 11.2 White-Space and Opportunity Mapping
  • 11.3 Product Evolution and Market Life Cycle Analysis
  • 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1 Mergers and Acquisitions
  • 12.2 Partnerships, Alliances, and Joint Ventures
  • 12.3 New Product Launches and Certifications
  • 12.4 Capacity Expansion and Investments
  • 12.5 Other Strategic Initiatives

13 Company Profiles

  • 13.1 Mealzo
  • 13.2 Newseum Lab
  • 13.3 OttoChef AI
  • 13.4 Dave's List
  • 13.5 SaladStop!
  • 13.6 Food for Health
  • 13.7 Little Lunches
  • 13.8 GrubTok
  • 13.9 Spoon Guru
  • 13.10 Tyana.app
  • 13.11 Calo
  • 13.12 Forki
  • 13.13 HelloFresh
  • 13.14 Factor
  • 13.15 Home Chef
  • 13.16 Sunbasket
  • 13.17 CookUnity
  • 13.18 Green Chef

List of Tables

  • Table 1 Global AI-Based Food Recommendation Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI-Based Food Recommendation Market Outlook, By Recommendation Type (2023-2034) ($MN)
  • Table 3 Global AI-Based Food Recommendation Market Outlook, By Personalized Meal Planning (2023-2034) ($MN)
  • Table 4 Global AI-Based Food Recommendation Market Outlook, By Nutritional Guidance & Diet Optimization (2023-2034) ($MN)
  • Table 5 Global AI-Based Food Recommendation Market Outlook, By Flavor & Taste Preference Matching (2023-2034) ($MN)
  • Table 6 Global AI-Based Food Recommendation Market Outlook, By Restaurant & Food Service Suggestions (2023-2034) ($MN)
  • Table 7 Global AI-Based Food Recommendation Market Outlook, By Grocery & Ingredient Recommendations (2023-2034) ($MN)
  • Table 8 Global AI-Based Food Recommendation Market Outlook, By Technology Approach (2023-2034) ($MN)
  • Table 9 Global AI-Based Food Recommendation Market Outlook, By Machine Learning Models (2023-2034) ($MN)
  • Table 10 Global AI-Based Food Recommendation Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 11 Global AI-Based Food Recommendation Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 12 Global AI-Based Food Recommendation Market Outlook, By Hybrid AI Systems (2023-2034) ($MN)
  • Table 13 Global AI-Based Food Recommendation Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 14 Global AI-Based Food Recommendation Market Outlook, By Cloud-Based Platforms (2023-2034) ($MN)
  • Table 15 Global AI-Based Food Recommendation Market Outlook, By On-Premise Solutions (2023-2034) ($MN)
  • Table 16 Global AI-Based Food Recommendation Market Outlook, By Mobile Applications (2023-2034) ($MN)
  • Table 17 Global AI-Based Food Recommendation Market Outlook, By Embedded Systems (2023-2034) ($MN)
  • Table 18 Global AI-Based Food Recommendation Market Outlook, By Application Context (2023-2034) ($MN)
  • Table 19 Global AI-Based Food Recommendation Market Outlook, By Health & Wellness (2023-2034) ($MN)
  • Table 20 Global AI-Based Food Recommendation Market Outlook, By Lifestyle & Convenience (2023-2034) ($MN)
  • Table 21 Global AI-Based Food Recommendation Market Outlook, By Sustainability (2023-2034) ($MN)
  • Table 22 Global AI-Based Food Recommendation Market Outlook, By Cultural & Regional Cuisine Adaptation (2023-2034) ($MN)
  • Table 23 Global AI-Based Food Recommendation Market Outlook, By End User (2023-2034) ($MN)
  • Table 24 Global AI-Based Food Recommendation Market Outlook, By Individual Consumers (2023-2034) ($MN)
  • Table 25 Global AI-Based Food Recommendation Market Outlook, By Restaurants & Food Service Providers (2023-2034) ($MN)
  • Table 26 Global AI-Based Food Recommendation Market Outlook, By Grocery Retailers & E-Commerce Platforms (2023-2034) ($MN)
  • Table 27 Global AI-Based Food Recommendation Market Outlook, By Nutritionists & Healthcare Providers (2023-2034) ($MN)
  • Table 28 Global AI-Based Food Recommendation Market Outlook, By Food Manufacturers (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) Regions are also represented in the same manner as above.