Conditional Generative Adversarial Networks (CGANs) are a type of machine learning model that generates new data based on specific conditions or labels. They consist of two neural networks: a generator that creates data and a discriminator that evaluates it. The generator learns to produce realistic outputs, while the discriminator learns to distinguish between real and generated data, both improving through competition. In CGANs, the generator receives additional information, such as class labels or attributes, allowing it to create data that meets certain criteria. This makes CGANs useful for tasks like image generation, where users can specify desired features, leading to more controlled and relevant outputs.