Heart disease is one of the leading causes of death worldwide, making early prediction and diagnosis extremely important. This review paper focuses on the use of machine learning techniques for predicting heart disease based on medical data. Various algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine are analyzed for their effectiveness in prediction. The system uses patient health parameters like age, blood pressure, cholesterol level, and heart rate to determine the risk of heart disease. A web-based application is also discussed, developed using Python for backend processing and HTML/CSS for user interaction. The results show that machine learning models can significantly improve prediction accuracy and assist doctors in decision-making. This paper highlights the importance of data preprocessing, model selection, and performance evaluation in building an efficient heart disease prediction system.