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GANs (generative opposing networks) are a technique for learning deep representations in the absence of a large amount of annotated training data. This is accomplished through the use of a competitive technique that employs two networks to generate background signals. GANs can use learned representations for a variety of applications, including image synthesis, semantic imaging, style transfer, super magnification, and segmentation. Images can be utilized in a variety of ways. Generative Adversarial Networks (GANs) are a unique class that has recently received a lot of interest due to the popularity of deep generative models. GANs implicitly distribute complex and high-resolution images, sounds, and data. However, due to inadvertently built network architecture, objective function usage, and optimization algorithm selection, significant difficulties such as mode collapse, inconsistencies, and instability develop while training GANs. In this paper, we conduct a thorough examination of the developments in GANs design and optimization strategies presented to address GANs difficulties. We provide intriguing study possibilities in this rapidly evolving area. GANs are a popular study topic due to their ability to generate synthetic data and the benefits of representations that can be understood regardless of the application. While various reviews for GANs in the image processing arena have been undertaken to date, none have focused on the review of GANs in multi-disciplinary domains. As a result, the utilization of GAN in interdisciplinary applications fields and its implementation issues were investigated in this survey by doing a thorough search for journal/research article connected to GAN.