Generative Adversarial Networks (GANs) have emerged as a powerful tool for addressing data scarcity and privacy concerns in various domains such as healthcare, finance, and security. This paper provides a comprehensive overview of GANs and their applications in synthetic data generation. We discuss the importance of synthetic data, the challenges associated with its generation, and techniques for improving GAN performance. Through case studies and experiments, we demonstrate the effectiveness of GANs in generating realistic and diverse synthetic data. We also examine ethical considerations surrounding the use of synthetic data and outline future directions and challenges in this rapidly evolving field. Overall, this paper highlights the potential of GANs to revolutionize data generation and addresses key considerations for their responsible deployment in sensitive domains.