In the field of deep learning, generative adversarial networks (GANs) have become a potent instrument, providing creative answers to a wide range of image-related problems. This paper explores the utilization of GANs for image augmentation, a technique pivotal in enhancing the robustness and generalization capabilities of deep learning models. We delve into the evolution of GAN architectures, highlighting seminal works such as the original GAN, deep-convolutional GAN (DC-GAN), conditional GAN (cGAN), and others, each contributing distinct advancements to the field. Furthermore, we discuss key applications of GANs across diverse domains, showcasing their efficacy in image generation, style transfer, super-resolution, and more. Focusing on image augmentation, we elucidate its significance in mitigating overfitting and improving model performance, particularly in scenarios with limited training data. In addition, we present notable applications of GAN-based image augmentation, encompassing fields such as healthcare, anomaly detection, and drug discovery. By synthesizing existing literature and empirical evidence, this paper offers insights into the integration of GANs for image augmentation, fostering a deeper understanding of their role in advancing the capabilities of deep learning systems.