The phenomena of iced line galloping in overhead transmission lines, caused by wind or asymmetric icing, can directly result in structural damage, windage yaw discharge of conductor, and metal damage, posing significant risks to the operation of power systems. However, the existing prediction methods for iced line galloping are difficult to achieve accurate predictions due to the lack of a large amount of iced line galloping data that matches real-world conditions. To address these issues, this paper studies the overhead iced transmission line galloping response prediction. First, the models of finite element, aerodynamic coefficient, and aerodynamic excitation for the iced conductor are constructed. The dynamic response of the conductor is simulated using finite element software to obtain a dataset of conductor galloping under different parameters. Secondly, a particle swarm optimization-conditional generative adversarial network (PSO-CGAN) based iced transmission line galloping prediction model is proposed, where the weight parameters of loss function in CGAN are optimized by PSO. The model takes initial wind attack angle, wind speed, and span as inputs to output prediction results of iced transmission line galloping. Then, based on the dynamics and galloping features of the conductor, the effects of different initial wind attack angles, wind speeds, and icing thickness on galloping are analyzed. Finally, the superior performance of the proposed model is verified through simulations.