Cardiovascular diseases (CVDs) including heart failure (HF) remain one of the main causes of mortality worldwide and contribute considerably to the burden of health systems. In this group of diseases, heart failure is one of the diseases with a significant rate of hospitalization as well as mortality, and causes poor quality of life for millions of patients globally. This study evaluates the use of several machine learning (ML) algorithms in the prediction, risk of hospitalization, and mortality among heart failure HF patients. Even more advanced models of prediction like XGBoost, Random Forest, and, AdaBoost achieved the most accuracy with several AUC-ROC scores of more than 0.80 shown across many datasets. However, despite their accuracy, the practical adoption of ML models in clinical settings is hindered by challenges in model interpretability, generalizability, and limited availability of diverse datasets. This paper focuses on the evaluation of all ML models used for HF prediction and prevention highlighting the necessity of using SHAP and LIME as interpretable machine learning models. Furthermore, it calls for the development of standardized evaluation frameworks and larger, more inclusive datasets to enhance the real-world applicability of ML-driven heart failure management solutions.