This research examines the application of machine learning and Explainable AI (XAI) methods to predict the distribution of flight delays in the context of air traffic management. With the increasing complexity and volume of data in the aviation industry, accurate delay predictions are key to improving operational efficiency and safety. This research explores various machine learning algorithms, including Support Vector Machine (SVM) and Random Forest, to model and predict flight delays based on historical data and influencing factors, such as weather conditions, air traffic, crew availability, and delay times. The results of this research are machine learning exploration in the form of support vector machine and random forest algorithms in predicting air traffic delays resulting in a model with good accuracy between the support vector machine and random forest algorithms. The support vector machine algorithm produces an accuracy of 0.9, while the random forest algorithm produces an accuracy of 0.8. and the application of Explainable AI (XAI) produces variables that can explain delay factors. By using XAI, you can identify which variables have the greatest influence on flight delays. The result is that when implementing XAI, weather variables are more influential, so they are able to predict more accurately when bad weather occurs.