Insufficient blood circulation throughout the body is referred to as heart failure. The heart normally becomes too weak or stiff to function properly when this occurs. Occasionally, congestive heart failure is used to describe it. Congestive heart failure life expectancy varies with disease severity, age, genetics, and other variables. As per the Centers for Disease Control and Prevention (CDC), nearly half of congestive heart failure patients survive for more than five years. Several studies have attempted to forecast the survival rate of patients with heart failure. However, most of these studies have utilized a dataset consisting of 299 rows and have failed to balance the dataset. The primary goal of this research is to build an explainable machine learning model that can predict the survival status of heart failure patients using machine learning algorithms. In this research, we used machine learning algorithms for prediction. Machine learning serves as a means for Artificial Intelligence systems to accomplish a multitude of tasks, among them being the prediction of output values from input data. We used the latest machine learning algorithms. The data is collected from Felege Hiwot referral hospital and Injibara general hospital. The algorithms used for prediction model are DT, LR, KNN, DNN, and XGBoost. According to the experimental findings, XGBoost outperforms other machine learning techniques by producing an AUC value of 0.93 using Grid Search HPO after SMOTE. Keyword: - Heart Failure; Survival Event; Follow-up Period; Synthetic Minority Oversampling Technique; Hyper-parameter Optimization; Model Explainability