The identification of cardiovascular disease is an important research topic that calls for precise and trustworthy classification methods. In order to guarantee clean and consistent input data for analysis, this study used sophisticated preprocessing techniques, such as noise removal and duplication deletion. Python was used to construct machine learning techniques for cardiovascular illness classification, including Naïve Bayes, Decision Trees, and K-Nearest Neighbor. Models such as HRFLM, stacked SVM, RSA_RF, and Ens (GNB + DT + KNN) were assessed using a wide range of performance criteria, including accuracy, precision, recall, sensitivity, and specificity. The precision, sensitivity, and specificity of the ensemble-based model Ens (GNB + DT + KNN) were consistently better than those of other methods. Both stacked SVM and RSA_RF produced competitive results; in terms of sensitivity and recall, stacked SVM marginally outperformed RSA_RF. On the other hand, the HRFLM model performed worse, especially in terms of recall and specificity. The results highlight how ensemble-based models, such Ens (GNB + DT + KNN), are resilient and adaptable in obtaining balanced and excellent performance across a range of evaluation measures. The potential of combining preprocessing methods with ensemble-based machine learning for precise and trustworthy cardiovascular disease classification is demonstrated by this study.