This paper introduces an ensemble heuristic–metaheuristic feature fusion learning (EHMFFL) algorithm for heart disease diagnosis using tabular data. The EHMFFL algorithm combines ensemble learning and feature fusion, utilizing seven base learners: support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), naive bayes (NB), decision tree (DT), and XGBoost. The primary objectives are to identify the most pertinent features for each base learner and aggregate the decision outcomes through ensemble learning. The performance of the EHMFFL algorithm is evaluated using the Cleveland and Statlog datasets, achieving an accuracy of 91.8% and 88.9%, respectively, surpassing state-of-the-art techniques. The algorithm's effectiveness is demonstrated through various performance metrics, including accuracy, precision, recall, F1 score, specificity, and ROC. The paper also includes a literature review, data gathering, detailed algorithm description, and a comparison with existing techniques, highlighting the EHMFFL algorithm's superior predictive capability in heart disease diagnosis.This paper introduces an ensemble heuristic–metaheuristic feature fusion learning (EHMFFL) algorithm for heart disease diagnosis using tabular data. The EHMFFL algorithm combines ensemble learning and feature fusion, utilizing seven base learners: support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), random forest (RF), naive bayes (NB), decision tree (DT), and XGBoost. The primary objectives are to identify the most pertinent features for each base learner and aggregate the decision outcomes through ensemble learning. The performance of the EHMFFL algorithm is evaluated using the Cleveland and Statlog datasets, achieving an accuracy of 91.8% and 88.9%, respectively, surpassing state-of-the-art techniques. The algorithm's effectiveness is demonstrated through various performance metrics, including accuracy, precision, recall, F1 score, specificity, and ROC. The paper also includes a literature review, data gathering, detailed algorithm description, and a comparison with existing techniques, highlighting the EHMFFL algorithm's superior predictive capability in heart disease diagnosis.