AI in Personalized Nutrition Market- By Type
AI in Personalized Nutrition Market- By Application
AI in Personalized Nutrition Market- By End User
AI in Personalized Nutrition Market- By Provider
AI in Personalized Nutrition Market- By Region
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global Artificial Intelligence in Personalized Nutrition Market Snapshot
Chapter 4. Global Artificial Intelligence in Personalized Nutrition Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: by Type Estimates & Trend Analysis
5.1. by Type & Market Share, 2024 & 2034
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Type:
5.2.1. Machine Learning
5.2.1.1. Supervised Learning
5.2.1.2. Unsupervised Learning
5.2.1.3. Reinforcement Learning
5.2.2. Deep Learning
5.2.2.1. Convolutional Neural Networks (CNN)
5.2.2.2. Recurrent Neural Networks (RNN)
5.2.2.3. Generative Adversarial Networks (GAN)
5.2.2.4. Transformers
5.2.3. Natural Language Processing (NLP)
5.2.3.1. Sentiment Analysis
5.2.3.2. Language Translation
5.2.3.3. Chatbots and Virtual Assistants
5.2.4. Computer Vision
5.2.4.1. Image Classification
5.2.4.2. Object Detection
5.2.4.3. Facial Recognition
5.2.4.4. Video Analysis
Chapter 6. Market Segmentation 2: by Application Estimates & Trend Analysis
6.1. by Application & Market Share, 2024 & 2034
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Application:
6.2.1. Meal Planning and Recommendations
6.2.1.1. Personalized Meal Plans
6.2.1.2. Recipe Suggestions
6.2.1.3. Dietary Restriction Considerations
6.2.2. Nutrient Analysis
6.2.2.1. Nutritional Deficiency Identification
6.2.2.2. Dietary Recommendations
6.2.2.3. Nutrient Optimization
6.2.3. Personalized Supplementation
6.2.3.1. Supplement Recommendations
6.2.3.2. Dosage and Timing Suggestions
6.2.3.3. Tracking and Monitoring
6.2.4. Allergen and Sensitivity Detection
6.2.4.1. Allergen Identification
6.2.4.2. Alternative Ingredient Suggestions
6.2.4.3. Personalized Dietary Restrictions
6.2.5. Health Monitoring
6.2.5.1. Biometric Data Analysis
6.2.5.2. Real-time Health Feedback
6.2.5.3. Behavioral Pattern Recognition
Chapter 7. Market Segmentation 3: By Provider Estimates & Trend Analysis
7.1. By Provider & Market Share, 2024 & 2034
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following By Provider:
7.2.1. Startups and Small Companies
7.2.1.1. AI-Powered Nutrition Startups
7.2.1.2. Innovative Tech Solutions
7.2.2. Established Tech Companies
7.2.2.1. Technology Giants
7.2.2.2. AI Platform Providers
7.2.3. Healthcare and Wellness Organizations
7.2.3.1. Hospitals and Medical Centers
7.2.3.2. Wellness Clinics and Centers
Chapter 8. Market Segmentation 4: by End User Estimates & Trend Analysis
8.1. By End User & Market Share, 2024 & 2034
8.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following By End User:
8.2.1. Individuals
8.2.1.1. Personalized Nutrition Apps
8.2.1.2. Smart Devices and Wearables
8.2.2. Fitness and Wellness Centers
8.2.2.1. Personal Trainers and Coaches
8.2.2.2. Nutritionist Services
8.2.3. Healthcare Providers
8.2.3.1. Hospitals and Clinics
8.2.3.2. Telehealth Platforms
8.2.4. Food and Beverage Industry
8.2.4.1. Food Manufacturers
8.2.4.2. Restaurants and Food Services
Chapter 9. Artificial Intelligence in Personalized Nutrition Market Segmentation 5: Regional Estimates & Trend Analysis
9.1. North America
9.1.1. North America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
9.1.2. North America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.1.3. North America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts By Provider, 2021-2034
9.1.4. North America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
9.1.5. North America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
9.2. Europe
9.2.1. Europe Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
9.2.2. Europe Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.2.3. Europe Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts By Provider, 2021-2034
9.2.4. Europe Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
9.2.5. Europe Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
9.3. Asia Pacific
9.3.1. Asia Pacific Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
9.3.2. Asia Pacific Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.3.3. Asia-Pacific Thermal Cyclers Asia-Pacific Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts By Provider, 2021-2034
9.3.4. Asia-Pacific Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
9.3.5. Asia Pacific Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
9.4. Latin America
9.4.1. Latin America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
9.4.2. Latin America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.4.3. Latin America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts By Provider, 2021-2034
9.4.4. Latin America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
9.4.5. Latin America Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
9.5. Middle East & Africa
9.5.1. Middle East & Africa Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
9.5.2. Middle East & Africa Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
9.5.3. Middle East & Africa Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts By Provider, 2021-2034
9.5.4. Middle East & Africa Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
9.5.5. Middle East & Africa Artificial Intelligence in Personalized Nutrition Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
Chapter 10. Competitive Landscape
10.1. Major Mergers and Acquisitions/Strategic Alliances
10.2. Company Profiles
10.2.1. Nutrino Health Ltd.
10.2.2. DayTwo Ltd.
10.2.3. Lumen
10.2.4. Nutrigenomix Inc.
10.2.5. Viome
10.2.6. Foodvisor
10.2.7. Baze Labs
10.2.8. GenoPalate
10.2.9. Habit
10.2.10. Nutraceutical Corporation
10.2.11. Nutrafol
10.2.12. Zoe
10.2.13. Healbe
10.2.14. FitGenie
10.2.15. Level Foods Inc.
10.2.16. Nutrunity
10.2.17. NutriAI
10.2.18. NutrinoTech
10.2.19. AIMEE Health
10.2.20. NutriMe
10.2.21. PreBiomics
10.2.22. NutrEval
10.2.23. NutriAdmin
10.2.24. Nutrissential
10.2.25. NutriPredict
10.2.26. Other Prominent Players
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.