Heart disease is a primary health concern worldwide and is responsible for many deaths every year. Early detection and timely management of risk factors can improve outcomes and reduce the burden of this disease. In this study, we explore using machine learning algorithms to predict the risk of heart disease using a dataset containing 14 medical characteristics. We compare the accuracy of six different classifiers, including Naive Bayes, Nearest Neighbors, Random Forest, Gaussian NB, Multinomial NB, and Decision Tree algorithms. Our results show that the Random Forest classifier performed the best with an accuracy of 89%. The use of machine learning in heart disease prediction can help healthcare professionals to make informed decisions and provide personalized care to patients. Developing accurate prediction models can improve health, reduce healthcare costs, and save lives.