The second most important organ in the human body after the first important brain is the heart. It helps blood pump and circulate through the body’s organs. Heart disease is found to be leading reasons of death across the globe. Data learning is beneficial for creating predictions based on new information, and it helps hospitals forecast diseases. A significant amount of patient-related data is kept on a monthly basis. Future disease forecasts can be based on the knowledge that has been saved. For patients’ clinical care to be simplified, it’s critical to diagnose and treat heart disease at very early stage. Heart disease can be predicted and detected early, which can reduce the risk of mortality and improve patient outcomes. Models of Machine learning produces encouraging outcomes in identifying cardiac disease. Investigation followed in this utilizes a python-based machine learning model is created because it is more reliable and facilitates the monitoring and configuration of various health monitoring applica1tions. Researchers introduce the use of categorical variables and the transformation of categorical columns when handling information. Techniques of artificial intelligence including logistic regression, random forest, and decision trees are employed for prediction using the KNN model and SVM. Use a diverse evaluation criterion to evaluate the effectiveness of these techniques, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). The accuracy scores of 99.9% and 99.03% that the decision tree and random forest algorithms attained are somewhat comparable, according to the results. However, the accuracy scores for the SVM and logistic regression methods were lower, coming in at 85.2% and 80.4%, respectively. Overall, this research work will demonstrate the power of python and machine learning in developing accurate and efficient prediction techniques for coronary artery disease, with the potential to improve patient outcomes and save lives.