Generative models, which have achieved disruptive applications in the fields of natural language processing and computer vision, are becoming the cornerstone of digital intelligence technologies, serving as a crucial engine driving the future development of intelligent aircraft technology. This paper reviews the application progress of aircraft technologies empowered with generative models. Firstly, the development history of generative model architectures is summarized. Detailed introduction to the fundamental principles and improvement directions of variational autoencoders, generative adversarial networks, diffusion models, and Transformers is provided. Secondly, typical applications and transformative impacts of generative models in aircraft aerodynamics, trajectory prediction, and target detection are generalized, with a focus on the development trends in key technologies of aircraft aerodynamic design, including parameterized modeling, aerodynamic prediction model and inverse design. Intelligent implementation methods of real-time trajectory prediction, complete trajectory prediction, collaborative trajectory prediction and prediction error compensation are studied. From the perspective of improving existing target detection methods, the roles of generative models in multi-scale fusion, super-resolution enhancement and data enhancement are analyzed. Finally, we propose future research directions for aircraft technologies empowered with generative models from the perspectives of model method and application scenario expansion. Development suggestions are proposed for building interpretable general models and promoting vertical domain applications.