Image-to-Image Translation is a task where models convert images from one representation to another, such as transforming sketches into realistic images or translating day-time images into night-time ones. This process involves learning mappings between different domains or styles, enabling applications like image enhancement, style transfer, and data synthesis. Techniques such as CycleGAN and pix2pix are commonly used for these tasks, showcasing the versatility of generative models in handling diverse image transformation problems.