Generative adversarial networks (GANs) offer an innovative solution for defect repair in three-dimensional models of railway engineering. This paper proposes a hybrid GAN framework that integrates multimodal data and engineering constraints to address complex three-dimensional model defects in railway engineering, such as cracks in rail welds and holes in tunnel linings. By embedding a finite element analysis verification layer and engineering specification constraints, this method ensures both geometric accuracy and compliance with mechanical performance and industry standards. Experiments show that this framework can effectively handle noise interference and partial missing data, significantly improving repair efficiency. However, the special nature of railway scenarios still poses challenges such as adaptability to extreme environments and lightweight deployment. In the future, it is necessary to further explore the combination of dedicated dataset construction and edge computing technology to promote the in-depth application of GANs in railway engineering.