This chapter offers a thorough overview of how generative adversarial networks (GANs) are used in ophthalmology and their transformative role in medical imaging, diagnostics, and predictive modeling. GANs, consisting of a generator and discriminator that compete in an adversarial learning process, produce highly realistic medical images that boost data-limited fields, and improve diagnostic accuracy. In ophthalmology, these networks have been utilized with optical coherence tomography (OCT), fundus photography, fluorescein angiography (FFA), and meibography to enhance image quality, eliminate artifacts, and create synthetic datasets for training algorithms. Advanced architectures such as CycleGAN, conditional GAN, and transformer-based VTGAN have shown superior results in generating noninvasive angiographic images, vessel segmentation, and detecting pathology. Beyond imaging, GANs are increasingly employed to predict postoperative outcomes, forecast therapeutic responses, and simulate surgical results. Despite their potential, challenges like mode collapse, spatial distortion, limited interpretability, and ethical concerns regarding synthetic data still exist. With ongoing improvements and proper regulation, GANs are set to become essential tools in precision ophthalmology, connecting clinical data science with patient-centered innovation.