A Generative Adversarial Network (GAN) is a machine learning framework that consists of two neural networks: the Generator and the Discriminator. The Generator creates synthetic data samples, while the Discriminator evaluates their authenticity against real data. This adversarial process leads to the Generator producing increasingly realistic data, making GANs highly effective for generating realistic images, videos, and other types of data.