Cardiovascular diseases (CVDs) remain a leading cause of global mortality, underscoring the need for reliable and interpretable predictive models to support early diagnosis and clinical decision-making. This study proposes a hybrid ensemble framework that integrates SMOTEENN resampling with a metric-weighted Voting Classifier to address class imbalance and improve prediction robustness. Using the publicly available Kaggle CVD dataset ( \({\approx } 70,000\) records), the dataset was split into 80% training and 20% testing using stratified sampling and we evaluated a diverse pool of base classifiers including Decision Tree, Logistic Regression, K-Nearest Neighbour, Random Forest, LightGBM, XGBoost, and Multilayer Perceptron. Performance metrics such as Accuracy, Precision, Recall, F1-score, Matthews Correlation Coefficient (MCC), and ROC-AUC were employed to provide a comprehensive evaluation. Results demonstrate that the proposed SMOTEENN + metric-weighted ensemble significantly outperformed the baseline (non-resampled) and existing models, improving accuracy from 72.6% to 96.9%, F1-score from 0.72 to 0.97, and ROC-AUC from 0.73 to 0.97. Moreover, the metric-weighting scheme allowed aggregation to be guided by clinically meaningful criteria, enhancing sensitivity and balanced classification.