In recent years, with the rapid advancement of technology and related topics such as electronic automation systems, associated algorithms, and software systems, artificial intelligence has increasingly permeated our daily lives. The aviation sector, by its very nature, is at the forefront of industries that adopt the latest technologies most quickly and most readily. This is because innovations in this field directly affect safety, security, cost, profitability, operational performance, and efficiency. Therefore, in the coming period, applications based on artificial intelligence methods are expected to be reflected more in the field of Aviation Management. However, the literature shows that research on this subject is lagging behind. The aim of this study is to reveal the social structure, conceptual structure, and intellectual structure of academic research conducted on artificial intelligence applications in the field of aviation management and to determine productivity in the field. The analyses were performed using the bibliometric method based on articles published on this subject in Web of Science using WosSviewer software. The analyses show that the number of studies conducted since Covid-19 has been increasing day by day. However, it has been assessed that there are not enough studies conducted in aviation management and its subtopics. Therefore, it can be stated that there are research gaps in the literature regarding the subtopics of aviation management. In light of the analyses performed, it was found that the countries with the most citations were China, the United States, and the United Kingdom; the universities with the most citations were Chinese; the most frequently used common words were “artificial intelligence,” “deep learning,” and “machine learning”; and the authors with the most citations were Abdel-Aty, Mohamed, Wang, Fei-Yue, and Aggarwal, Vaneet. “IEE Transactions on Intelligent Transportation Systems,” followed by “Transportation Research Part: Emerging Technologies” and “Safety Science,” were determined to have the most citations among the top three journals. Among aviation management topics, studies on air traffic management, advanced predictive models, data models, reinforcement learning, risk management, and blockchain were most prevalent. The existence of new and varied examples of artificial intelligence applications in the field of aviation management indicates that there will be an increase in studies in this area in the literature in the coming period.