Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. This chapter explores the sophisticated uses of GANs in domain adaptation, image translation, and high-resolution image synthesis, highlighting their role in overcoming significant challenges in computer vision. In the first section, we discuss about the background of Generative Adversarial Network for different domains. In the second section, we discuss about the development of GAN designs like StyleGAN, ProGAN and ESRGAN, highlighting the ways in which these architectures have gradually made it possible to synthesize extremely detailed, high-definition images. We illustrate how these models produce aesthetically pleasing and contextually appropriate images from random noise vectors by concentrating on the mechanisms of adversarial training, latent space manipulation. In the third section, we concentrate on image translation, where GANs help to convert input images from one visual domain to another. We present recent developments in this area and demonstrate its use in tasks such as super-resolution, face attribute modification, and semantic segmentation. In the last section, we explore domain adaptation, where GANs are essential for moving knowledge between domains with little to no labeled data. We investigate how GAN-based techniques handle issues like domain shifts, resulting in efficient image translation across a variety of visual styles and applications, from medical imaging to autonomous driving, through the examination of CyCADA.