Heart disease is one of the most consequential illnesses currently understood. Because of the widespread dissemination of information, numerous techniques and algorithms have been developed to better predict the prognosis of patients with cardiac disease. Using a dataset provided by Kaggle, this paper describes 13 crucial processes. This work was done by Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes, and Random Forest, which produced the most accurate findings with an accuracy of 93%. Everyone’s comparative statement algorithms are also presented in the implementation section paper. This study also uses model validation techniques to design the most appropriate model to fit the current situation.