In the recent years, cardiovascular diseases (CVDs) have emerged as the primary cause of death worldwide, including in India. The application of machine learning (ML) methods in clinical research is on the rise, facilitating the analysis of large and complex datasets and aiding in the automation of heart disease prediction. In health care, ML algorithms are enhancing life support systems and aiding in the early detection of CVDs. Given the heart’s essential function in circulating blood throughout the body, accurate cardiovascular disease prediction is crucial in medical practice. Healthcare facilities generate vast amounts of patient data, which can be harnessed for disease prediction and treatment planning. Machine learning models, such as decision trees, support vector machines (SVMs), and Logistic Regression have been utilized to predict heart disease outcomes. These models facilitate quick and precise detection, potentially reducing the mortality rate associated with CVDs. By applying patient data and ML algorithms, healthcare professionals can better the performance (accuracy) of heart disease prediction. This research primarily aims to utilize machine learning techniques to predict coronary heart disease, offering better tools for informed clinical decisions. We will be doing a proper study utilizing several algorithms of machine learning to extract which algorithm performs best out of all the others and why it does so.