A great heterogeneity comes because of computer power and technical improvements, particularly in the identification of human heart failures, there is a significant amount of heterogeneity in the field of medical sciences. The leading cause of mortality globally is heart disease. In the treatment of heart illness, a broad variety of cutting-edge technologies are used. Since many medical workers lack the knowledge and skills essential to treat patients appropriately, it is the most common problem in medical institutions. They thus make their own judgments, which usually have catastrophic consequences and negative outcomes. By utilizing Machine Learning techniques and data mining approaches, forecasting of cardiac disease is being done to address these issues. It is now straightforward to do automatic analysis in these facilities since health centers are so important in this. It is possible to identify cardiac issues by examining the patient’s numerous health signs. It is now one of the most dangerous cardiac conditions for people and has a very negative impact on human life. Accurate and prompt diagnosis of human heart disease can be essential for stopping heart failure in its earliest stages and improving the patient’s prognosis. To forecast cardiac disease, we have employed many factors. Age, blood pressure, fasting blood sugar test, gender identity, cerebral palsy, and other variables are employed. We utilized an internal dataset for our research study. To predict heart disease, we have used the same dataset and five distinct approaches. We employed Softmax Regression, Independence Bayes, Rough Forest, SVM, and KNN as our methods. By completing certain preprocessing and dataset standardization, we also calculated their accuracy on the conventional heart disease dataset. This study examines whether the method for predicting heart disease from health indicators is more accurate. The most precise is Rough Forest, according to experimentation, at 96%.