AI 기반 식품 추천 시장 예측(-2034년) : 추천 유형, 기술 어프로치, 도입 형태, 적용 분야, 최종사용자 및 지역별 세계 분석

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