The healthcare sector manages billions of people globally and generates enormous amounts of data. Better insights are being produced by the machine learning-based algorithms as they analyse the multidimensional medical information. In this work, many cutting-edge Supervised Machine Learning algorithms that are specifically employed for illness prediction are applied to classify a cardiovascular dataset. According to the findings, Decision Tree classification model outperformed Naive Bayes, Logistic Regression, Random Forest, SVM, and KNN based methods in its ability to predict cardiovascular illnesses. The Decision Tree delivered the best outcome with a 73% accuracy rate. This method could assist medical professionals anticipate the onset of cardiac problems and provide the proper therapy.