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According to new WHO statistics, heart disease is the top reason of death worldwide, killing 17.9 million people each year. This is a growing number. One of the most wellknown issues in clinical offices is that no two professionals have the same knowledge and talent when serving their patients. Researchers are utilizing data mining and machine learning techniques to overcome these difficulties by using predictive analytics to anticipate the risk of heart problems. This study examines the accuracy of various machine learning methods, including Logistic Regression, Naive Bayes, Decision Trees, Support Vector Machines, Neural Networks, and Stochastic Gradient Descent in the prediction of heart disease based on various factors and symptoms such as gender, age, chest pain, and blood sugar using appropriate data. The research entails applying a typical data mining approach to accurately uncover relationships between numerous data sources to predict heart disease. These machine learning algorithms take less time and are more accurate at predicting heart illness, which will lower the global convergence of essential life.