In the healthcare industry, managing information is essential; data mining in the healthcare industry, managing information is essential; data mining techniques are the best option for solving challenging issues. Analytics for big data apply hybridization optimization strategies to make decisions on health information, considering a significant portion of the communication issues in the healthcare network. Given that heart disorders are considered a significant global cause of death for both men and women, this article focused on the data mining of heart disease and related topics. Therefore, people should be aware of the potential symptoms of heart disease. Using the patient’s medical history, we developed an algorithm to estimate the likelihood of a heart disease diagnosis We used several machine learning methods, such as logistic regression and KNN, in conjunction with a Random Forest classifier to predict and categorize the heart disease patient. Employed an extremely helpful method to enhance the model’s forecast accuracy for individual heart attacks. The suggested model was strong enough and could correctly guess if a certain person had heart disease using logistic regression and random forest, with 88% and 85% accuracy, respectively.