• Chetan .R. Ingole
• Subodh Tirpude
• Chandan Majumdar
• Sumit Jade
• Saurav Hingade

In the era of digital health, the quest for personalized well-being has led to innovative approaches in leveraging technology for dietary guidance. This research introduces a comprehensive Health-Based Food Recommendation System designed to optimize wellness through intelligent and personalized nutritional recommendations. The system utilizes advanced machine learning algorithms to analyze user health data, dietary preferences, and lifestyle factors. By amalgamating nutritional science with cutting-edge technology, the system tailor’s food recommendations to individual needs, promoting a balanced and health-conscious approach to nutrition. The research outlines the architecture and functionality of the recommendation system, emphasizing its adaptability to diverse user profiles. Through extensive data analysis and user feedback, the system refines its recommendations over time, ensuring continuous improvement in precision and relevance. The integration of real-time health monitoring further enhances the system's ability to respond dynamically to changing health conditions and goals. Key features include a user-friendly interface, daily meal plans, and interactive nutritional insights. The system encourages informed decision-making by providing detailed information on recommended foods, their nutritional benefits, and potential health impacts. A series of user trials demonstrates the system's efficacy in positively influencing dietary habits and improving overall well-being.

"Optimizing Wellness: A Health-Based Food Recommendation System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.i306-i311, March-2024, Available :http://www.jetir.org/papers/JETIR2403840.pdf

"Optimizing Wellness: A Health-Based Food Recommendation System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppi306-i311, March-2024, Available at : http://www.jetir.org/papers/JETIR2403840.pdf