Heart disease continues to be a major global health concern, accounting for a significant number of premature deaths each year. Early detection can improve survival rates, yet traditional diagnostic methods are time-consuming and often dependent on expert interpretation. This study applies machine learning techniques to clinical data to develop a predictive model capable of estimating heart disease risk. Various algorithms—including Logistic Regression, Random Forest, Support Vector Machine (SVM), and XGBoost—were evaluated. The results show that ensemble models deliver the highest accuracy, demonstrating strong potential for supporting clinical decision-making.