Among the main causes of mortality for the general public is cardiovascular illness. The likelihood of survival for patients with cardiac problems is significantly impacted by late detection. Life-threatening heart disorders are recognized to be impacted by many elements, like age, gender, blood sugar, cholesterol, and heart rate. However, because there are so many variables, it can be challenging for an expert to assess each patient while considering this information. The opportunity to apply machine learning (ML) and deep learning (DL) algorithms to predict cardiac diseases is investigated in this review paper. The benefits and drawbacks of traditional machine learning methods, such as Decision Tree classifier, Support Vector Machine algorithm, Random Forest classifier, ANN and few more in the context of the forecast of heart disease, are examined and evaluated. Additionally, the research explores one of the rapidly developing subjects of deep gaining knowledge (DL), looking at developments in convolutional and recurrent neural networks (RNNs) that are especially suited for analyzing medical information, including Electronic Health Records (EHRs) and electrocardiograms (ECGs). The effectiveness of ML and DL techniques, emphasizing the precision, comprehensibility, and applicability of each are being evaluated in this paper. The state of heart disease prediction using ML and DL is critically examined in this review, with an emphasis on both the field's achievements and shortcomings. Conclusions are drawn by highlighting the significance of these AI-powered strategies will transform the prevention of heart disease and enhance patient outcomes, as well as ethical considerations and future goals for this quickly developing subject.