This research offers a fresh innovative, mathematical modeling technique of CVDs as an innovative machine learning model. Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting improve prediction accuracy which are combined in the study. The model was assessed using a broad set of predictor variables that included demographic characteristics, life style and clinical parameters. The proposed model had better accuracy with 87%, precision 85%, recall 83% as well as F1-score of 84%. The test results on the Random Forest model provided the highest accuracy of 90%, while Gradient Boosting was on the second place with 88%, then SVM 85%, and Logistic Regression 82%. Comparing with the existant algorithms, corresponding increases of prediction accuracy and risk factor discrimination were identify. Feature importance analysis integrating was able to take the model to the next level by excluding unproductive covariate which affected the risk of CVD. This research contributes to the development of models for cardiovascular health problems in particular and, therefore, the development of early warning systems and risk estimation models. The study highlights the possibility of improving diagnostic and other care-related decisions as well as prevention intervention plans.