This paper presents a comprehensive study on Generative Adversarial Networks (GANs), a powerful deep learning technique for generating realistic synthetic data. The work focuses on understanding the core architecture of GANs, which consists of two competing neural networks—the generator and the discriminator—trained through an adversarial learning process. The study explores the working principles of GANs and highlights their advantages over traditional generative models, particularly in producing high-quality and detailed outputs. Various applications of GANs are discussed, including image generation, image enhancement, medical imaging, and data augmentation. In addition, the paper analyzes key challenges associated with GANs, such as training instability, mode collapse, and high computational requirements. It also reviews recent advancements and techniques aimed at improving GAN performance and stability. This work aims to provide a clear understanding of GAN technology and its growing importance in modern artificial intelligence, while also identifying future research directions for enhancing its effectiveness in real-world applications.