The prediction of hazardous asteroids near Earth is critical for planetary defense and avoiding any possible impacts. This study investigates the use of five ensemble models, XGBoost, Gradient Boost, CatBoost, Voting Classifier, and Random Forest, as well as four standalone machine learning models, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, and Decision Tree, to improve the prediction accuracy of identifying potentially hazardous asteroids. With 92% accuracy and 91% precision, Random Forest performed better than other models. It was the preferred choice for predicting hazardous asteroids because of its capacity to handle the huge dataset with efficiency and its ability to manage non-linear data patterns. Additionally, XGBoost and CatBoost provided high accuracy at low computational costs, making them suitable for real-time monitoring. KNN, on the other hand, did not perform well, and SVM’s high processing time made it less useful. In particular, Random Forest ensemble model performed better at predicting hazardous asteroids.