Heart health prognosis is critical to cardiovascular medicine, as it helps predict the risk of developing cardiovascular diseases and their subsequent complications. By understanding the underlying factors that contribute to heart health, healthcare professionals can develop targeted interventions to improve patient outcomes. This research focuses on the critical aspect of heart health prognosis, which is essential for predicting the risk of cardiovascular diseases and their complications. Understanding the factors affecting heart health enables targeted interventions by healthcare professionals to enhance patient outcomes. Ensemble learning, a powerful machine learning technique, is introduced in this study to predict the immunity of heart disease patients, enhancing prediction accuracy and robustness by integrating multiple models. The ensemble model combines predictions from three distinct machine-learning algorithms: support vector machines (SVMs), random forests (RFs), and gradient boosting machines (GBMs). Training is carried out on a diverse dataset encompassing heart failure and non-heart failure patients, with evaluation on an independent test set. The results exhibit the superior performance of the ensemble learning model compared to individual algorithms on the test set, achieving an impressive accuracy of 94%, sensitivity of 93%, and specificity of 95%. These promising results suggest that the proposed ensemble learning model holds significant potential for accurately predicting heart disease immunity. The development of such predictive models can assist healthcare professionals in identifying individuals at higher risk of heart disease post-COVID-19 recovery, enabling timely interventions and improved patient outcomes, consequently mitigating the long-term healthcare burden attributed to COVID19-related cardiac complications.