Insufficient blood circulation throughout the body is referred to as heart failure, which makes the heart to become too weak or stiff to function properly. Congestive heart failure life expectancy varies with disease severity, age, genetics, and other variables. Nearly half of all congestive heart failure patients will live past 5 years, according to the Centers for Disease Control and Prevention. Several studies have attempted to predict 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 a machine learning model that can predict the survival status of heart failure patients using 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, K-nearest neighbors (KNN), and XGBoost. Experimental results show that KNN with Bayesian and grid search performed best with area under the curve (AUC) value 0.87. XGBoost performed best with AUC value 0.87 with random search and Bayesian hyper-parameter optimization techniques.