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Abstract: Heart disease is a significant health concern worldwide, necessitating effective predictive models for early detection and intervention. In this study, we employ five popular machine learning algorithms: Random Forest, Logistic Regression, Decision Tree, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), to develop predictive models for heart disease classification. The dataset used in this analysis undergoes extensive data cleaning, include handling missing values, standardizing features, and addressing class imbalance. Each algorithm is implemented and evaluated using appropriate performance metrics such as accuracy, precision, recall, and F1-score. Comparative analysis of these models provides insights into their effectiveness in predicting heart disease, aiding healthcare professionals in making informed decisions for patient care and management.