This study presents a hybrid, metaheuristic-driven optimization framework for power hyperparameter tuning in predictive modeling based on large-scale an nual health examination data. Different from conventional grid and random search strategies, the proposed method directly incorporates particle swarm optimization, artificial bee colony, and gravitational search algorithm into the training pipeline of multiple machine learning models, enabling adaptive ex ploration of high-dimensional parameter spaces under clinical data constraints. The approach was evaluated on a comprehensive dataset comprising 93 clin ical attributes and 1,000 patient records, with a specific focus on ischemic stroke risk prediction. Random Forest, decision tree, support vector machine, and logistic regression models were optimized using the proposed hybrid struc ture and benchmarked against baseline configurations. Experimental results demonstrate consistent and statistically significant reductions in mean squared error, mean absolute error, and root mean squared error, alongside improve ments in R2 and classification accuracy exceeding 99% for optimized logistic regression models, while maintaining computational efficiency suitable for rou tine clinical deployment. Beyond performance gains, the study introduces a stacked ensemble architecture guided by metaheuristic-tuned base learners, enhancing model robustness and generalization across training and indepen dent test sets. These findings demonstrate the practical novelty of integrating swarm and numerical optimization into clinical predictive pipelines, providing a scalable and domain-agnostic solution for high-accuracy risk decision support in preventive healthcare and other data-intensive applications.