REVIEW article
Front. Nutr.
Sec. Nutrition and Food Science Technology
Volume 12 - 2025 | doi: 10.3389/fnut.2025.1636980
This article is part of the Research TopicAI-Driven Advances in Personalized Nutrition through Optimization in Food ManufacturingView all 3 articles
Artificial Intelligence in Personalized Nutrition and Food Manufacturing: A Comprehensive Review of Methods, Applications, and Future Directions
Provisionally accepted
- 1Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, India
- 2UCD School of Computer Science and CeADAR, University College Dublin, Dublin, Ireland
- 3ICAR-National Academy of Agricultural Research Management, Hyderabad, India
- 4Anglia Ruskin University, Cambridge, United Kingdom
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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, personalized nutrition, Food manufacturing, machine learning, Federated learning, predictive analytics
Received: 28 May 2025; Accepted: 03 Jul 2025.
Copyright: © 2025 Agrawal, Goktas, Kumar and Leung. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Man-Fai Leung, Anglia Ruskin University, Cambridge, United Kingdom
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