• Aditya Bandgar
• Anup Kesarwani
• Harsh Lambe
• Sahil Pawar
• Dr.A R Deshpande

In today’s fast-paced environment, maintaining a nutritious and balanced diet can be challenging, especially with the rise of lifestyle-related diseases such as obesity, diabetes, and cardiovascular conditions. Many people find it difficult to select diets that both fulfil their nutritional requirements and align with their personal preferences and health objectives. Existing solutions often lack personalization, making it difficult to cater to specific health conditions and dietary restrictions. This project proposes the creation of a personalized Diet Recommendation System, which offers tailored dietary suggestions based on individual health data, preferences, and goals. By incorporating user-specific details such as age, weight, health conditions, and food preferences, the system generates customized meal plans Utilizing machine learning algorithms and nutritional databases, the system ensures that the recommendations are balanced and nutritionally adequate. The solution not only supports users in making informed food choices but also aids in long-term health management. With continuous feedback and updates, the system can adjust to evolving health metrics, ensuring the recommended diet remains appropriate and beneficial. In summary, the diet recommendation system addresses common dietary challenges by delivering customized meal plans that encourage healthier eating habits, ultimately contributing to improved overall health and well-being.

Diet Recommendation System Personalized Nu trition, Machine Learning, Health Management.

"A Brief Review on Diet Recommendation System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.f428-f432, March-2025, Available :http://www.jetir.org/papers/JETIR2503579.pdf

"A Brief Review on Diet Recommendation System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppf428-f432, March-2025, Available at : http://www.jetir.org/papers/JETIR2503579.pdf