Predicting the onset of heart disease accurately is essential for early diagnosis and prevention of this global pandemic. The paper suggests a hybrid method to improve heart disease prediction. The research examines several machine learning (ML) models for detecting heart illness and assesses how well they predict heart disease. To enhance precision, the hybrid method employs not one but many machine learning methods. The hybrid method employs SVMs, random forests, and neural networks as its machine- learning algorithms. When it comes to classification, SVM is a very effective method. The data points are separated into classes, and the optimal hyperplane to do this is the goal. SVM can learn the boundaries and patterns between various risk variables and efficiently categorize people as having heart disease or not. Random forests are a kind of ensemble learning that uses several individual decision trees to make a final determination. The characteristics used to construct each decision tree are chosen at random. Each decision tree contributes to the overall forecast, which is then aggregated. Due to their versatility, random forests may be used to the prediction of cardiovascular disease. Neural networks are a kind of algorithm that takes their cues from the way the human brain operates. They are made up of several layers of artificial neurons working together to learn intricate patterns from data. Medical diagnosis is only one field where neural networks have been shown to be useful. In the hybrid method, neural networks may learn complex associations between risk factors and cardiovascular disease and provide reliable prognoses based on this information. The hybrid method enhances the accuracy of heart disease prediction by combining the benefits of various machine-learning techniques