Modeling realistic time-varying and frequency-selective channels is critical for designing the wireless systems that are robust to fading channels in different environments. Modeling a wireless channel is a time-consuming process which involves measurement campaigns in various environments and then fitting the right statistical model for each measurement scenario. In this chapter, we discuss few methods to generate wireless channel using generative AI approaches. Firstly, we discuss a method to generate channel based on the generative adversarial network (GAN). This approach is helpful in generating time-varying and frequency-selective channels for the scenarios which do not have a known statistical model. Next, we delve into the diffusion-based channel modeling that uses U-Net architecture, noise-conditioned score network (NCSN), and score-stochastic differential equation (SDE). Further, we explore the latest trends in generative AI with large-scale models for wireless channel models and downstream applications like channel estimation, beam prediction, and interference detection.