Mainly related to the cardiovascular system, brain, kidney, and peripheral arteries, the disease is called heart disease. Heart disease can have many causes, but high blood pressure and atherosclerosis are the main ones. Additionally, structural and physiological changes in the heart with age are largely responsible for heart disease, which can occur even in healthy individuals. They are not put to use or employed in any way. If these data were investigated and examined, diseases may be predicted or perhaps prevented. By using images of cancer cells to train a dataset, diseases like cancer may be identified and their stage can be forecasted. Similarly, to that, factors like cholesterol, diabetes, heart rate, etc. can be used to predict heart disease. It is difficult and dangerous to predict cardiac disorders. We noticed that sometimes there are multiple approaches used to solve a problem. It varies depending on the circumstances. The fact that most of the data are sparse or absent since they weren't recorded with the intention of analysis presents another difficulty. With data from four hospitals in four distinct locations, we, therefore, set out to determine which strategy would be best for forecasting the diseases. This study compares the effectiveness of various data mining methods for predicting cardiac disease, including, K-nearest neighbors, Random Forest, and Multi-layer Perceptron, Logistic Regression. The effectiveness of prediction for each approach utilized is reported after an analysis of the Data Mining methodologies. The outcome demonstrated that heart problems can be predicted with greater than 97 percent accuracy.