The integration of fog computing with health data analytics signifies a paradigm shift in the field of healthcare, offering the potential for streamlined and prompt analysis of patient welfare. The increasing volume of health data necessitates the development of efficient analytical models in fog computing settings. The objective of this research is to examine the integration of fog computing and health data analytics, specifically emphasizing the utilization of hyperparameter tuning and grid search techniques to enhance optimization approaches. Hyperparameter tuning and grid search are two techniques utilized in machine learning to optimize the performance of models. These methods are employed in the context of health data analytics inside fog computing with the objective of improving accuracy, reducing latency, and enhancing resource efficiency. Our research endeavors to provide significant contributions to the advancement of adaptable and responsive healthcare systems, therefore promoting enhanced patient outcomes in the era of data-driven decision-making.