This chapter explores how artificial intelligence (AI) is transforming the diagnosis, treatment, and management of corneal diseases and refractive surgery. It examines the incorporation of machine learning (ML) and deep learning (DL) techniques into advanced ophthalmic imaging methods, including slit-lamp photography, anterior-segment optical coherence tomography (AS-OCT), specular microscopy, and in vivo confocal microscopy. AI-based algorithms are increasing diagnostic accuracy for conditions like keratoconus, infectious keratitis, and dry eye disease, while also enhancing outcomes in corneal transplantation and refractive surgery. The chapter discusses the role of convolutional neural networks (CNNs), U-Net architectures, and ensemble models in automating image segmentation, disease classification, and surgical planning. Furthermore, AI applications are improving refractive surgery parameters and predicting postoperative results with high precision. These innovations mark a shift toward precision medicine and personalized corneal care, allowing for earlier diagnosis, more predictable treatments, and better patient outcomes.