Generative Adversarial Networks (GANs) are a type of machine learning model consisting of two neural networks: the generator and the discriminator. The generator creates data resembling real-world samples, while the discriminator evaluates its authenticity, distinguishing between real and fake data. This adversarial process helps GANs generate highly realistic outputs, such as images, videos, and audio. GANs are widely used in fields like image synthesis, data augmentation, and creative content generation, pushing the boundaries of AI creativity and innovation.