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Review
. 2025 Jul 23:12:1636980.
doi: 10.3389/fnut.2025.1636980. eCollection 2025.

Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions

Kushagra Agrawal #  1 , Polat Goktas #  2 , Navneet Kumar  3 , Man-Fai Leung  4
Affiliations
Review

Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions

Kushagra Agrawal et al. Front Nutr. .

Abstract

Artificial Intelligence (AI) is emerging as a key driver at the intersection of nutrition and food systems, offering scalable solutions for precision health, smart manufacturing, and sustainable development. This study aims to present a comprehensive review of AI-driven innovations that enable precision nutrition through real-time dietary recommendations, meal planning informed by individual biological markers (e.g., blood glucose or cholesterol levels), and adaptive feedback systems. It further examines the integration of AI technologies in food production, such as machine learning-based quality control, predictive maintenance, and waste minimization, to support circular economy goals and enhance food system resilience. Drawing on advances in deep learning, federated learning, and computer vision, the review outlines how AI transforms static, population-level dietary models into dynamic, data-informed frameworks tailored to individual needs. The paper also addresses critical challenges related to algorithmic transparency, data privacy, and equitable access, and proposes actionable pathways for ethical and scalable implementation. By bridging healthcare, nutrition, and industrial domains, this study offers a forward-looking roadmap for leveraging AI to build intelligent, inclusive, and sustainable food-health ecosystems.

Keywords: artificial intelligence; federated learning; food manufacturing; machine learning; personalized nutrition; predictive analytics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Diagram illustrating the framework for AI-driven personalized nutrition involving five interconnected domains: Data Science and AI, Healthcare Professionals, Policy and Regulations, Industry and Technology, and Nutrition Science. Each domain includes specific inputs and outputs contributing to outcomes like personalized nutrition plans, improved public health strategies, data-driven chronic disease prevention, and ethical AI systems. The central focus is on AI-driven personalized nutrition, integrating data for predictive models and personalized dietary plans.
Figure 1
A conceptual framework illustrating interdisciplinary collaboration in AI-driven personalized nutrition. The model integrates contributions from data science, healthcare, nutrition, industry, and policy to produce ethically grounded, clinically valid, and context-sensitive dietary solutions.
A colorful infographic illustrating technological innovations across various application domains. Domains include Healthcare, Food Manufacturing, Personalized Nutrition, Smart Packaging and IoT, Hospitality, and Food Safety. Innovations listed are Federated Learning, Predictive Analytics, Deep Learning, IoT Monitoring, AI Inventory Forecasting, and Micro/Nano-motors. Strategic outcomes emphasize privacy-preserving care, operational efficiency, adaptive recommendations, product traceability, food waste reduction, and allergen detection.
Figure 2
Conceptual mapping of emerging innovation trajectories across key application domains. The figure illustrates how advanced technologies such as federated learning, IoT, and AI-driven systems intersect with healthcare, food production, packaging, and hospitality, enabling targeted outcomes in privacy, sustainability, and efficiency.
See this image and copyright information in PMC

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