Heart disease remains a leading cause of death globally, making the development of accurate and understandable diagnostic models crucial. This research introduces an integrated approach that combines advanced ensemble learning techniques like XGBoost and AdaBoost with methods to enhance transparency, specifically SHAP (“SHapley Additive exPlanations”). Using the Heart Disease Dataset, the models were fine-tuned to achieve an impressive accuracy of 95.6%. The study details the mathematical underpinnings of these ensemble methods, including loss functions and weight optimization. SHAP values were used to interpret the model’s predictions, providing clear insights into which features are most important and making the model’s decisions more transparent. This framework not only boosts diagnostic accuracy but also ensures that the predictions can be understood by clinicians, addressing the need for both precision and clarity in healthcare applications. The experimental results showed significant improvements over traditional models, proving the effectiveness of this integrated method. Future research will look into applying this approach to other cardiovascular datasets and improving the explainability further to better integrate into clinical practice.