Flight delays are a persistent issue in the aviation industry, affecting passenger satisfaction, airline operations,and airport efficiency. These delays can be caused by various factors such as weather conditions, technicalissues, air traffic congestion, or crew unavailability. Unpredictable delays not only inconvenience travelers butalso lead to significant financial losses for airlines and logistical disruptions across the network. As the volumeof air traffic continues to grow, there is an urgent need for systems that can forecast potential delays accuratelyand in advance. This project proposes a machine learning-based flight delay prediction system that leverageshistorical flight data along with additional features such as weather reports, flight schedules, and airport trafficinformation. Multiple machine learning algorithms—including Random Forest, Decision Tree, and XGBoost—were trained and evaluated to determine the most effective model for predicting delays. Data preprocessingtechniques such as feature selection, normalization, and label encoding were applied to ensure data quality andmodel performance. The model predicts whether a given flight is likely to be delayed, helping airlines andpassengers plan accordingly. The results demonstrate that machine learning can significantly enhance theaccuracy of delay predictions compared to traditional rule-based systems. By integrating predictive analyticsinto airline operations, the system can aid in resource allocation, improve passenger communication, and reducecascading delays across routes. This approach not only offers a practical solution to a real-world problem butalso highlights the potential of artificial intelligence in optimizing air travel operations.