This study explores the application of machine learning to predict heart failure decompensation, addressing its growing global health burden. By accurately identifying early signs of decompensation through algorithms such as XGBoost and Random Forest, healthcare providers can tailor timely and personalized interventions. This reduces complications, avoids unnecessary hospital readmissions, and ultimately alleviates the financial and resource-related strain on both patients and healthcare systems. The study also highlights common challenges faced in predictive modeling, including missing data, selection bias, and model interpretability issues. To overcome these, future research should focus on rigorous external validation, the inclusion of a broader range of clinical and demographic variables, and the use of interpretability tools such as SHAP analysis to better understand complex model behavior. This proactive, data-driven approach supports more effective patient management, optimizes healthcare delivery, and promotes efficiency in resource allocation. Through a systematic literature review, this work contributes to advancing heart failure management by integrating advanced analytical techniques into clinical decision-making.