Heart disease prediction is crucial for saving lives. A new machine learning model was created using data from heart disease patients. The model combines Random Forest and Decision Tree algorithms to predict heart disease with 88.7% accuracy. This hybrid model is more effective than using either algorithm alone. By inputting user data, the model can accurately predict the likelihood of heart disease, helping in early detection and prevention.