This research introduces an optimized ensemble learning approach for heart disease prediction, integrating the robustness of ensemble learning algorithms with Bayesian optimization for hyperparameter tuning and Shapley Additive Explanations (SHAP) for interpretability. The ensemble classifiers considered include AdaBoost, random forest, and extreme gradient boosting (XGBoost). The study evaluates the performance of these models on the Cleveland and Framingham datasets, demonstrating that the optimized XGBoost model achieved the highest performance, with specificity and sensitivity values of 0.971 and 0.989 on the Cleveland dataset, and 0.921 and 0.975 on the Framingham dataset, respectively. The SHAP technique is used to interpret the predictions, providing insights into which features are most influential in the model's decision-making process. The study highlights the importance of effective hyperparameter tuning and interpretability in clinical applications, making the proposed approach a promising tool for early detection and treatment of heart disease.This research introduces an optimized ensemble learning approach for heart disease prediction, integrating the robustness of ensemble learning algorithms with Bayesian optimization for hyperparameter tuning and Shapley Additive Explanations (SHAP) for interpretability. The ensemble classifiers considered include AdaBoost, random forest, and extreme gradient boosting (XGBoost). The study evaluates the performance of these models on the Cleveland and Framingham datasets, demonstrating that the optimized XGBoost model achieved the highest performance, with specificity and sensitivity values of 0.971 and 0.989 on the Cleveland dataset, and 0.921 and 0.975 on the Framingham dataset, respectively. The SHAP technique is used to interpret the predictions, providing insights into which features are most influential in the model's decision-making process. The study highlights the importance of effective hyperparameter tuning and interpretability in clinical applications, making the proposed approach a promising tool for early detection and treatment of heart disease.