Cardiovascular Disease (CVD) remains a primary cause of the death in the world, which underlines the importance of early detection and intervention of the illness. By using a dataset comprising various clinical parameters, including the demographic information, previous medical reports, results of diagnostic tests etc., Machine learning (ML) and Deep learning (DL) models can be trained and evaluated to accurately classify individuals into CVD and non-CVD groups. Available heart health data can be analyzed using different types of classifiers including the support vector machines, random forest classifiers, logistic regression, convolutional neural networks. These ML and DL approaches enhance CVD detection and thereby offer valuable insights for improving diagnostic accuracy and patient outcomes in clinical practice. These techniques can improve heart disease diagnosis, but they must be optimized and interpretability issues must be addressed for practical deployment in healthcare settings.