One of the leading causes of death and disability around the world is coronary artery disease. A vast number of people around the world are afflicted by this terrible disease. This is especially true when one considers death rates and the enormous number of people who suffer from heart disease. This sickness cannot be diagnosed using conventional methods. In clinical data analysis, cardiovascular disease prediction is recognised as one of the most essential topics. The healthcare business generates enormous amounts of data. Using machine learning to develop a medical diagnosis system for predicting heart disease is more accurate than the traditional method. Machine learning (ML) has proven to be helpful in reducing the massive amounts of data generated by the healthcare industry's decision-making. Several studies provide only a brief glimpse into how machine learning can be used to predict cardiac disease. Using machine learning approaches, we offer a method for identifying relevant traits that can be used to improve the accuracy of cardiovascular disease prediction. Various combinations of features and recognised classification methods are used to build the prediction model. With the hybrid model for heart disease prediction, we're able to get a higher level of performance while maintaining a high degree of accuracy.