According to WHO data, heart disease is to blame for one-third of all deaths globally each year. It is estimated that cardiovascular disease claims the lives of around 17.9 million people each year throughout the world. According to the European Cardiology Society(ECS), there are around 26 million people worldwide who have been diagnosed with cardiac illness, with an additional 3.6 million being diagnosed each year. In the first two years after diagnosis, around half of all patients with heart disease die and heart disease treatment accounts for about 3% of total health-care spending. To effectively predict heart illness, you'll need a slew of different tests. Improper forecasting may be the result of medical staff lacking sufficient expertise. It may be difficult to diagnose cancer at an early stage. The surgical treatment of heart disease is tough and this is much truer in developing countries that lack medical professionals, diagnostic equipment and other resources essential for accurate diagnosis and treatment of heart patients. It would help avoid catastrophic heart attacks and improve patient safety if cardiac failure risk could be precisely assessed. Machine learning algorithms can indeed be effective at detecting diseases provided they are properly taught with relevant data. To compare prediction models, there are publicly available datasets on heart disease. Scientists can now build the most accurate prediction model possible by combining machine learning and artificial intelligence, which are both on the rise. Cardiovascular Disease (CVD) mortality has been on the rise in both adults and children,