Heart disease remains a leading cause of mortality globally, creating a critical need for accurate and early risk identification to facilitate effective prevention and management. While traditional diagnostic methods are foundational, they often face challenges in handling the complex, multifactorial nature of cardiovascular risk factors, which can limit their predictive power. To address these limitations, we propose and validates a sophisticated ensemble machine learning model for the prediction of heart disease. The proposed methodology employs a Stacking Classifier, an advanced supervised learning technique that integrates the predictive capabilities of multiple diverse algorithms. The architecture consists of Decision Tree, AdaBoost, and Gradient Boosting classifiers as base-level models, with a Logistic Regression classifier serving as a meta-model to synthesize their outputs for a final prediction. The propounded model demonstrates higher performance, achieving an overall accuracy of 98.05%. Furthermore, the models delivered consistently high performance, achieving precision, recall, and F1-scores of 0.98 for both patient categories—those with and without heart disease—demonstrating balanced and highly accurate classification. The outcomes demonstrate that this stacking ensemble strategy could be leveraged as a robust and impactful clinical decision support system. By integrating propounded model into clinical workflows, healthcare providers can enhance early risk stratification, enable more timely and personalized patient management and pave the way for improved health outcomes.