Gan (Generative Adversarial Network) is a powerful framework in AI that learns to generate new data with characteristics similar to training data. It involves two competing Neural Networks: a generator that creates new examples, and a discriminator that tries to distinguish real data from generated data. This adversarial process drives both networks to improve, producing increasingly realistic outputs.