Accurate and efficient predictive models for the early diagnosis and risk assessment of heart disease are still very important on the ground of global health. In this study, Heart Disease is predicted using various machine learning classifiers such as Random Forest, Naïve Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbors, XGBoost and Neural Network. These models were created and verified on a publicly available dataset of heart diseases using important performance metrics like accuracy, precision, recall. Random Forest was found to be the most accurate classifier among the strategies and ensemble learning was demonstrated to be the best technique of dealing with complex medical data. Additionally, feature importance analysis was conducted to see critical feature affecting heart disease prediction. The result shows that machine learning techniques can be used as effective methods to contribute to the improvement of diagnostic accuracy and help with clinical decision making. This study has strengthened the use of data driven methods in terms of cardiovascular disease detection and prevention. The next test of a neural network machine architecture would involve more clinical parameters, better feature selection, and the exploration of deep learning architectures for improving predictive capabilities.