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

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 preci...

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Bibliographic Details
Published in:Frontiers in nutrition (Lausanne) Vol. 12; p. 1636980
Main Authors: Agrawal, Kushagra, Goktas, Polat, Kumar, Navneet, Leung, Man-Fai
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media S.A 23.07.2025
Subjects:
artificial intelligence
federated learning
food manufacturing
machine learning
Nutrition
personalized nutrition
predictive analytics
machine learning
artificial intelligence
food manufacturing
predictive analytics
federated learning
personalized nutrition
ISSN:2296-861X, 2296-861X
Online Access:Get full text
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Summary: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.
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Reviewed by: Pratik Nayi, National Pingtung University of Science and Technology, Taiwan
Edited by: Branko Velebit, Institute of Meat Hygiene and Technology, Serbia
These authors have contributed equally to this work
Chandani Popalia, Junagadh Agricultural University, India
ISSN:2296-861X
2296-861X
DOI:10.3389/fnut.2025.1636980