Heart-related diseases, also known as cardiovascular diseases (CVDs), have emerged as the most life-threatening disease, not just in India but all over the world. The heart-related diseases account for the majority of deaths that have occurred around the globe over the last few decades. Therefore, there is a need for a system that is dependable, accurate, and practical in order to detect such disorders in a timely manner so that appropriate treatment may be administered. The algorithms and methods of machine learning have been applied to a variety of medical datasets in order to automate the research of extensive and complicated data sets. We developed a method for determining whether or not a patient is likely to be diagnosed with a heart disease by utilising the patient's medical history in our model. This allowed us to construct a heart disease prediction system. A dataset that was gathered from the archives of every international university was used in conjunction with a number of supervised machine learning methods. These methods were used to make predictions regarding cardiac disease.The highest performance was obtained using KNN (accuracy = 93.3%, precision = 100%, sensitivity = 80%) , when the value of k=1.