In order to more accurately predict flight delays at different times of the year, flight delay prediction trends was investigated using operational and meteorological data from Atlanta Airport in the United States for the year 2023. A CA-PCA-Informer flight delay prediction model, incorporating correlation analysis (CA), principal component analysis (PCA), and the Informer model, was proposed. Mean absolute error (MAE) and root mean square error (RMSE) were utilized as evaluation metrics to assess the prediction error. The findings reveal that the CA-PCA-Informer model outperforms simpler combined models, demonstrating the lowest error compared to the CA-PCA-LSTM and CA-PCA-GRU models, with MAE and RMSE reductions of 20.2% ~ 20.7% and 12.7% ~ 14.1%, respectively. The CA-PCA-Informer model is particularly effective for one-hour ahead predictions, providing decision-makers with more accurate flight delay trends to enhance efficient flight operations.