P. Lavanya 1, M. Chetan Kumar2, S. Sairaj3, M. Bharath4, R. Tharak Rama5 03 3(1) This paper presents an intelligent Diet and Fitness Recommender System that integrates Machine Learning ML mathematical optimization and conversational artificial intelligence through the Model Context Protocol MCP and Large Language Models LLMs The system estimates calories burned during physical activity using a trained Artificial Neural Network ANN regression model taking into account physiological parameters such as age weight height heart rate body temperature gender and workout duration Predicted caloric expenditure is then used to drive a Linear Programming LP optimization module that generates personalized diet plans maximizing protein intake under caloric and macronutrient constraints An LLM is integrated via MCP as an interactive conversational agent that coordinates backend analytical tools interprets user queries and presents recommendations in natural language The system achieves an ANN Mean Absolute Error MAE of 28 kcal an R score of 093 a Nutrition Constraint Satisfaction Rate CSR of 97 and a System Usability Scale SUS score of 82 out of 100 across a 25participant user study Experimental outcomes confirm that combining predictive analytics LP optimization and conversational AI delivers a more personalized accurate and engaging health recommendation experience than conventional approaches Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026