<p>Heart disease remains a major global health challenge, underscoring the need for predictive models that are both accurate and interpretable to support early diagnosis and clinical decision-making. Using Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) in this study allowed for a deeper level of understanding of model predictions as well as provided viable alternative ways to overcome the challenges associated with black-box classifiers. Using a soft-voting ensemble technique that combined representations for Random Forest (RF), Decision Tree (DT), and Support Vector Machines (SVM) was applied to evaluate a publicly available heart disease dataset, containing 5110 records with each record containing 76 attributes. A total of 14 clinically relevant features were identified and evaluated using an 80/20 train-test split for the experimental setup, with results further validated by 5-fold cross-validation. The proposed soft-voting ensemble model achieved an accuracy of 89.10%, precision of 83.90%, recall of 95.20%, and F1-score of 89.90%, out-performing all individual baseline models and many of the recent XAI-enhanced deep learning models. The combination of SHAP and LIME allowed for both global and local interpretations of the model’s outputs, providing interpretable insights as to features like cp, thal, ca, and oldpeak contributed to the predictions and aligning these features with known cardiovascular risk factors. The results of this research demonstrate how interpretable (or explainable) ensemble-learning mechanisms can confer highly reliable, clinically useful heart disease risk predictions.</p>