Generative Adversarial Networks (GANs) are a powerful AI framework consisting of two neural networks—a generator and a discriminator—working in competition. Introduced by Ian Goodfellow in 2014, GANs generate realistic synthetic data by learning from real data. The generator creates data, while the discriminator evaluates its authenticity, creating a feedback loop that improves results. GANs are widely used for tasks like image generation, deepfake creation, and data augmentation. Their ability to produce highly realistic outputs has revolutionized fields such as design, entertainment, and medical imaging.