In recent years, Cardiovascular diseases (CVDs) have been a major health issue. Due to CVDs, around 17.9 million deaths are occurring annually. Therefore, early detection of the disease and treating it is very necessary. This paper addresses the need for efficient early diagnosis and intervention to reduce the burden of heart disease. The paper emphasizes the ability of machine learning in predicting heart disease development based on identifiable risk factors. The study shows the transformative impact of machine learning algorithms in heart disease prediction, which improves healthcare outcomes and reduces heart disease incidents. In this paper, the machine learning model is trained using several widely used and most popular classification algorithms. The models are evaluated using different evaluation metrics. Random Forest gave the best result with an accuracy of 91.80%, thus outperforming other models.