Heart failure is a serious medical condition that affects millions worldwide, and early prediction is essential for timely intervention and better patient outcomes. While machine learning models have demonstrated strong predictive capabilities in healthcare, many high-performing models, such as Support Vector Machines (SVM), function as black boxes, making them difficult to interpret in clinical settings. This study examines how Explainable AI (XAI) techniques can enhance transparency in heart failure prediction.Using a publicly available dataset from Kaggle, we preprocess the data with label encoding, feature scaling and Hyperparameter tuning before training various machine learning models for binary classification. Our results indicate that the Support Vector Classifier (SVC) with a Linear kernel achieves the highest predictive accuracy. However, to improve interpretability, we compare its performance with explainable models like Decision Trees and apply post-hoc explanation techniques such as SHAP (SHapley Additive Explanations) and Permutation Importance.Through this comparative analysis, we highlight the trade-off between model accuracy and interpretability, offering insights into the feasibility of XAI-driven models in real-world clinical decision-making. Our findings reinforce the importance of developing AI systems that not only perform well but also provide understandable and trustworthy insights for medical professionals.