Generative Adversarial Networks (GANs) have become a powerful paradigm in artificial intelligence (AI), captivating researchers across various domains. Hence, this chapter focuses on applications of GANs in diverse domains and provides a comprehensive review of types of GANs, applications, tools, advantages, disadvantages, challenges, and some open research problems to be addressed by the researchers. The methodology involves gathering and analyzing studies from academic literature and authentic websites. By synthesizing insights from these sources, this review offers a structured overview to guide future research directions and promote advancements in this field. The relevance of this review lies in its thorough exploration of how GANs are applied across multiple domains. This chapter identifies the strengths and weaknesses inherent in different GAN architectures and discusses the tools and methodologies used for implementation. Moreover, by outlining the challenges like training instability, biases in generated data, and computational demands, this chapter aims to offer actionable insights for researchers and practitioners.