In recent years, health-related diseases have become the top cause of death globally, as the rapid development in Machine Learning (ML) has had a significant impact on the healthcare industry. Misclassification of heart disease can have severe repercussions for patients. Requiring enhanced precision in diagnostic tools, researchers have developed numerous approaches dedicated to early prediction, thereby addressing the imperative need to reduce mortality risks associated with heart disease. This paper explores several machine learning and deep learning models, along with various hybrid algorithms proposed for the early detection of Heart Disease. This study utilizes various available datasets and discusses multiple preprocessing and feature extraction methods, along with their evaluation metrics.