Authors : M. Bhanu Sridhar; P. Bhavani; N. S. V. A. Sahithi. Iswarya; M. Vagdevi; S. K. Ayesha Siddiqa; Y. Vennela

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/mry78zwk

Scribd : https://tinyurl.com/22ebbeeu

DOI : https://doi.org/10.38124/ijisrt/26apr1092

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

Abstract : Gemini AI provides personalized dietary guidance for pregnant women, where maternal nutrition plays a vital role in fetal development and maternal health. Traditional diet charts are often static and fail to consider individual factors such as trimester, dietary preferences, and medical conditions. This system adopts a hybrid AI approach by combining dataset-driven meal planning with transformer-based Natural Language Processing and generative AI. It consists of a Meal Planning Module that generates personalized diet schedules and a Chatbot Module that offers interactive guidance and support. The system uses BERT for understanding user queries, FLAN-T5 for generating responses, and Gemini AI for handling complex queries, while Lang Chain manages the interaction between models and maintains conversation context. Overall, the system enhances the accuracy of recommendations and user experience compared to traditional methods, supporting better nutrition management and informed dietary decisions during pregnancy.

Keywords : Pregnancy Nutrition, Meal Planning, Transformer Models, BERT, FLAN-T5, Gemini AI, Chatbot, Maternal Health, Personalized Diet Recommendations, Gestational Diabetes, Lang Chain.

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