<p>Clinical decision-making depends on the ability to anticipate ischemic heart disease (IHD) accurately and interpretably. Despite their excellent accuracy, machine learning models are not often accepted in medical applications due to their black-box nature. To diagnose IHD, this study suggests an Explainable Artificial Intelligence (XAI) architecture that combines explainable models with the Enhanced Squirrel Search Optimization (ESSO) method for feature selection. The proposed Enhanced Squirrel Search Optimization (ESSO) introduces adaptive exploration mechanisms to efficiently identify an optimal subset of clinically relevant features. By integrating ESSO-based feature selection with Random Forest classification and explainable AI techniques (SHAP and LIME), the framework simultaneously improves predictive performance and provides interpretable insights for clinical decision support. We use SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) methodologies to offer clear explanations into the model’s predictions, building on earlier research that showed how effective squirrel search is at selecting the best clinical features. With KNN-based imputation, normalization, categorical encoding, outlier handling, and SMOTE-based class balancing for managing missing data, the UCI heart disease dataset is utilized for both training and validation. Random Forest is used to classify the chosen features, and domain experts assess the derived explanations for clinical relevance. With precise visual and numeric explanations of contributing features including cp., oldpeak, thal, and ca., the suggested model achieves an accuracy of 98.4%. By bridging the gap between interpretability and high-performance machine learning, our research makes the model appropriate for practical use in clinical settings.</p>