Heart failure (HF) is a multifaceted clinical condition associated with high morbidity and mortality rates. It is an increasing public health concern, impacting millions globally and placing considerable strain on healthcare systems. In recent decades, there has been a growing interest in using machine learning techniques to predict HF outcomes. Hence, this study aims to explore the clinical and demographic characteristics associated with HF outcomes using a comprehensive dataset obtained from Kaggle. The dataset, “Heart Failure Clinical Records.csv,” was preprocessed to address missing values and prepared for analysis. Feature importance analysis and correlation matrix computations were conducted to identify significant predictors of death events among HF patients, including age, serum creatinine, and ejection fraction. Various machine learning models, such as logistic regression, random forest, support vector machine, and gradient boosting machine, were employed to predict death events. The results revealed varying levels of performance among the models, with some demonstrating promising accuracy and predictive power. However, further refinement of these predictive models is warranted to enhance clinical decision-making and patient care in HF management. Overall, this study underscores the value of data-driven approaches in understanding HF outcomes and highlights the necessity for ongoing research in this field.