Air traffic management refers to the activities required for the efficient and safe management of the national air system (NAS) for each country. This concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various methods (e.g., statistical and fuzzy techniques) and data mining algorithms (e.g., neural network) have been used to solve the issues of air traffic management (ATM) and delay the minimization problems. However, each of these techniques has some disadvantages, such as overlooking the data, computational complexities, and uncertainty. The present study aimed to increase ATM efficiency using the deep learning approach. The main research objective was to propose a deep learning model with the application of a long short-term memory-based deep learning model in order to increase the predictive accuracy in short daily and long-term annual windows by enhancing deep learning (two-dimensional). In addition, the deep model output was transferred to the extreme learning machine fast learning deep neural machine in order to calculate the estimated time of arrival real-time based on other similar input data, including the NAS data, bureau of transportation statistics system, and automatic dependent surveillance-broadcast system. The final results indicated the increased accuracy of ATM compared to other studies.