The significant cause of death in the modern era is heart disease. People today engage in unhealthy behaviors like stress, alcohol use, coffee overuse, smoking, and inactivity. Since high blood pressure contributes to heart disease, some people may have pre-existing cardiac issues. Clinical data analysis faces a significant problem when predicting cardiovascular diseases. The healthcare industry produces enormous amounts of data, employing machine learning (ML) techniques for prediction and decision-making. Using machine learning as a tool, medical researchers and professionals may accurately and precisely identify and detect disease. Since forecasting cardiovascular disease (CVD) is difficult, so we should automate the procedure to reduce risks and alert the user in the upcoming years. Utilizing a number of machine learning techniques, including Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), and some XG Boost approach, this initiative tries to recognize the cardiac disease early and avoid problems.