Authors : Utkarsh Singh; Laxmi Ahuja

Volume/Issue : Volume 10 - 2025, Issue 5 - May

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Google Scholar : https://tinyurl.com/3886wph8

DOI : https://doi.org/10.38124/ijisrt/25may1102

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : Personalized nutrition has become essential for both prevention and overall health in this day and age, too. However, the effectiveness of current dietary recommendation systems in satisfying a variety of user needs is limited by their frequent lack of scalability, interactivity, and adaptability. An ML-powered nutrient recommendation system built with MERN (MongoDB, Express.js, React.js, and Node.js) stack offers a novel solution to these problems in this paper. To personalize dietary advice, the proposed system mixes specific information provided directly by users with algorithms trained on huge databases of nutrition prepared using very sophisticated machine learning algorithms.

Keywords : Personalized Nutrition, Machine Learning (ML), Nutrient Recommendation System, MERN Stack, Dietary Recommendations, Data-Driven Healthcare, Scalable Web Applications, Digital Health Applications.

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