Generative Adversarial Networks (GANs), a prominent subclass of generative models, have gained significant attention in recent years for their ability to replicate complex real-world data. Originating from the concept of a two-player zero-sum game, GANs pit a generator and a discriminator against each other, with the success of one offset by the failure of the other. This framework has driven remarkable advancements across domains such as computer vision and image generation. In this review, we introduce the fundamental concepts of GANs, provide an overview of their major variants, and compare them with other generative models. We discuss the breadth of their applications, analyze different GAN architectures, and situate our findings within the context of prior research.