This study presents a data-driven approach to predict flight departure delays caused by weather conditions. The research focuses on predicting flight delays for three major international airports: Incheon International Airport (ICN) in South Korea, John F. Kennedy International Airport (JFK) in the United States, and Chicago Midway International Airport (MDW) in the United States. The datasets used span over 10 years and include flight information at six different time intervals (2, 4, 8, 16, 24, and 48 hours) prior to flight departure. The datasets consist of 1,569,879 instances for ICN, 773,347 for JFK, and 404,507 for MDW. The study employs a range of machine learning and deep learning approaches, including Decision Tree, Random Forest, Support Vector Machine, K-nearest neighbors, Logistic Regression, Extreme Gradient Boosting, and Long Short-Term Memory (LSTM) to predict flight delays. The models achieved accuracy rates of 0.749 for ICN, 0.852 for JFK, and 0.785 for MDW in 2-hour predictions. For 48-hour predictions, the accuracy rates were 0.748 for ICN, 0.846 for JFK, and 0.772 for MDW. The study also discusses the importance of predicting flight delays over extended time frames, as international flights cover vast distances and have long durations. The results show that the proposed models can effectively predict flight delays, with the Random Forest model performing best for ICN, and the LSTM model performing best for JFK and MDW. The study also highlights the importance of weather data in predicting flight delays and the need for further research to improve the accuracy and generalizability of the models. The findings suggest that the proposed models can be applied to various transportation-related domains, including ocean vessel delays, vehicle operation restrictions, and outdoor construction work stoppages.This study presents a data-driven approach to predict flight departure delays caused by weather conditions. The research focuses on predicting flight delays for three major international airports: Incheon International Airport (ICN) in South Korea, John F. Kennedy International Airport (JFK) in the United States, and Chicago Midway International Airport (MDW) in the United States. The datasets used span over 10 years and include flight information at six different time intervals (2, 4, 8, 16, 24, and 48 hours) prior to flight departure. The datasets consist of 1,569,879 instances for ICN, 773,347 for JFK, and 404,507 for MDW. The study employs a range of machine learning and deep learning approaches, including Decision Tree, Random Forest, Support Vector Machine, K-nearest neighbors, Logistic Regression, Extreme Gradient Boosting, and Long Short-Term Memory (LSTM) to predict flight delays. The models achieved accuracy rates of 0.749 for ICN, 0.852 for JFK, and 0.785 for MDW in 2-hour predictions. For 48-hour predictions, the accuracy rates were 0.748 for ICN, 0.846 for JFK, and 0.772 for MDW. The study also discusses the importance of predicting flight delays over extended time frames, as international flights cover vast distances and have long durations. The results show that the proposed models can effectively predict flight delays, with the Random Forest model performing best for ICN, and the LSTM model performing best for JFK and MDW. The study also highlights the importance of weather data in predicting flight delays and the need for further research to improve the accuracy and generalizability of the models. The findings suggest that the proposed models can be applied to various transportation-related domains, including ocean vessel delays, vehicle operation restrictions, and outdoor construction work stoppages.