Cardiovascular diseases remain a significant global health concern, necessitating efficient and accurate prediction methods for early detection and personalized interventions. This paper proposes an ensemble model to predict cardiovascular disease by combining different classifiers to achieve a high level of accuracy. This research uses an integrated cardiovascular disease risk assessment based on the data set involving one or more characteristics such as age group, gender, depression, exercise regime, overall health, and more. The developed proposed ensemble model is a blend of Neural Networks, k-nearest neighbors (KNN), Decision Trees, and Random Forest where each algorithm works best on different fronts. The first step is the data preprocessing where the missing values are handled and features are normalized, ensuring the dataset is best suited to the model. Following this, the ensemble model is trained and tested on the cardiovascular disease dataset and achieves an accuracy level of 91.7%. Through the integration of KNN’s similarity-based selection on candidates, Decision Tree’s propositional decision map, and Random Forest’s ineffectiveness against noisy variables, as well as the profiling of Neural Network for non-linearity and intricate patterns unveiling, the created prediction model was both comprehensive and accurate.