The article provides a comprehensive overview of Generative Adversarial Networks (GANs), covering their introduction, taxonomy, variants, limitations, and applications. GANs are discussed in various fields, including natural language processing, architectural design, text-to-image conversion, image-to-image translation, 3D object production, audio-to-image conversion, and prediction. The article highlights the importance of GANs in identifying false images, particularly in the context of face forgeries, to ensure visual integrity and security. It also explores the development of diverse assessment techniques to evaluate GAN models' efficacy and scope. Key advancements in GANs, such as the creation of human facial photographs that are difficult for humans to identify, known as "Deepfakes," are discussed. The ethical, moral, and legal concerns associated with these technologies are addressed, emphasizing the need for forensic models to detect new GAN-generated images. The article reviews the basic GAN architecture, including the roles of the generator and discriminator, and the mathematical formulation of GANs. The review covers various GAN variants, such as Pix2Pix, CycleGAN, DCGAN, BIGGAN, PGGAN, WGAN, and StyleGAN, detailing their architectures, loss functions, and applications. It also discusses the challenges and limitations of GANs, including training difficulties, data handling issues, system instability, and false predictions, and proposes potential solutions. The article concludes with a discussion on future research directions, emphasizing the need for further development in analyzing GAN variants, improving synthetic information generation, and addressing face forgeries. It provides a detailed taxonomy of GAN models, highlighting their progress from 2015 to 2022, and offers insights into the latest advancements and applications of GANs.The article provides a comprehensive overview of Generative Adversarial Networks (GANs), covering their introduction, taxonomy, variants, limitations, and applications. GANs are discussed in various fields, including natural language processing, architectural design, text-to-image conversion, image-to-image translation, 3D object production, audio-to-image conversion, and prediction. The article highlights the importance of GANs in identifying false images, particularly in the context of face forgeries, to ensure visual integrity and security. It also explores the development of diverse assessment techniques to evaluate GAN models' efficacy and scope. Key advancements in GANs, such as the creation of human facial photographs that are difficult for humans to identify, known as "Deepfakes," are discussed. The ethical, moral, and legal concerns associated with these technologies are addressed, emphasizing the need for forensic models to detect new GAN-generated images. The article reviews the basic GAN architecture, including the roles of the generator and discriminator, and the mathematical formulation of GANs. The review covers various GAN variants, such as Pix2Pix, CycleGAN, DCGAN, BIGGAN, PGGAN, WGAN, and StyleGAN, detailing their architectures, loss functions, and applications. It also discusses the challenges and limitations of GANs, including training difficulties, data handling issues, system instability, and false predictions, and proposes potential solutions. The article concludes with a discussion on future research directions, emphasizing the need for further development in analyzing GAN variants, improving synthetic information generation, and addressing face forgeries. It provides a detailed taxonomy of GAN models, highlighting their progress from 2015 to 2022, and offers insights into the latest advancements and applications of GANs.