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Since the mortality rate is high due to cardiac issues, an in time invoke and timely diagnosis are essential in treatment. a rapid rate and it’s very Important and concerning to predict any such sudden strokes beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. This project mainly focuses on which patient is more likely to have a abnormal heart health based on various medical attributes. We prepared a cardiac spectator through which the patient is likely to be diagnosed with abnormal heart health or not using the medical history of the patient. We used different algorithms of machine learning such as CNN and classify the patient heart health . A quite Helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart health in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a abnormal heart health in a particular individual by using supervised learning which showed a good accuracy . So a quiet significant amount of pressure has been lift off by using the given model in finding the probability of the classifier to correctly and accurately identify the heart health and risk factor detention.. This project gives us significant knowledge that can help us predict the patients with abnormal heart health..