Generative Adversarial Networks (GANs) have revolutionized artificial intelligence by enabling the generation of realistic and diverse data through a competitive training process between two neural networks. They have practical applications in various fields, including image generation, text synthesis, and medical imaging, but face challenges such as training instability and ethical concerns. Ongoing research aims to address these limitations and explore new potentials, solidifying GANs' transformative impact on technology and industry.