The increasing prevalence of heart diseases has driven the development of automated arrhythmia classification systems using machine learning and electrocardiograms (ECG). This paper presents a novel ensemble learning method for classifying multiple arrhythmia types using 12 lead ECG signals through a blending technique. The framework employs a predetermined meta model from foundation models, while the remaining models serve as potential base estimators, ranked by accuracy. Using sequential forward selection and meta-feature augmentation, the system determines an optimal base estimator set and creates a meta-dataset for the meta-model, which is optimized through grid search with k-fold cross-validation. Experiments conducted with seven diverse machine learning algorithms (Adaptive boosting, Extreme gradient boosting, Decision trees, 1 2 3 4 5 6 7 8 9 K-nearest neighbors, Logistic regression, Random Forest, and Support vector machine) demonstrate that the proposed blending solution, utilizing an LR meta-model with three optimal base models, 11 achieves superior classification accuracy of 96.48%, offering an effective tool for clinical decision support.