Prediction of Delay in Flights using ML Techniques: A Review

Flight delays are a persistent challenge for the aviation industry, affecting passengers, airlines, and airport operations worldwide. Predicting delays in advance can significantly improve resource management, enhance passenger satisfaction, and optimize airline operations. Machine Learning (ML) techniques, such as machine learning and deep learning, have emerged as promising tools for accurately predicting flight delays by analyzing vast datasets with complex patterns. This review provides a comprehensive examination of AI-based methods used in delay prediction, focusing on techniques like regression models, neural networks, decision trees, and ensemble learning approaches. The paper examines the integration of various data sources, including weather conditions, air traffic, flight schedules, and historical delay records, to improve prediction accuracy.