Nutritional adequacy during pregnancy requires mothers to follow a well-balanced, personalized diet plan supporting maternal and fetal well-being. However, formulating such plans is challenging because of the combinatorial aspect of food selection and competing nutritional needs. This study offers an integrated framework of algorithms for intelligent menu planning specific to pregnant women. To measure the net optimizing value of meal recommendation programs within set nutrition limits, this study evaluated three AI programs, such as Q-Learning, SARSA, and Monte Carlo Tree Search (MCTS). Each algorithm was optimized through hyperparameter tuning to maximize a fitness function based on nutrient sufficiency, distribution, and adherence to predefined limits. Q-learning showed the fastest adaptation and performed best at elevated learning rates. In contrast, SARSA exhibited stable, more consistent behavior over extended training periods and performed best at alpha = 0.4 and epsilon = 0.35. MCTS provided better performance than both reinforcement learning methods by achieving the highest fitness score through a tree-based search concerning optimal exploration weight and simulation count. These findings support the claim that each algorithm performs best in specific areas, such as Q-Learning excels at learning new information, SARSA is proficient with fixed policies, and MCTS can easily solve complex combinations of variables and meet limits. The comparative results highlight that the choice of an algorithm must be aligned with the system's goals concerning efficiency, consistency, or comprehensiveness. This study develops AI-based dietary assistance systems and demonstrates the possibilities of customized algorithms designed for effective nutrition planning in maternal healthcare.
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