Traditional sports nutrition relies on standardized formulations that fail to ad- dress individual physiological and metabolic differences. This study introduces an AI- driven framework for personalized sports nutrition that integrates biochemical data, ingredient properties, and physiological response modeling. Machine learning tech- niques, including Random Forest, XGBoost, and Artificial Neural Networks, are used to predict nutrient–performance relationships, while Genetic Algorithms and Bayesian Optimization optimize ingredient ratios. Emphasis is placed on data integrity, model interpretability, and food safety compliance. The results demonstrate the potential of precision, AI-based supplement design to enhance endurance, recovery, and overall athletic performance..
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