Artificial intelligence (AI) is revolutionizing ophthalmology by leveraging machine learning (ML) and deep learning (DL) to enhance disease detection, prognostication, and personalized treatment planning. This entry first outlines core AI technologies—including convolutional neural networks, transfer learning, and explainable AI—that underpin recent advances in automated analysis of fundus photography, optical coherence tomography, and slit-lamp images. It then reviews clinical applications across subspecialties: AI-driven screening for diabetic retinopathy and age-related macular degeneration; algorithmic detection of glaucomatous optic neuropathy and prediction of visual field progression; and anterior segment analysis for keratoconus, cataract grading, and ocular surface tumors. This entry also explores AI’s role in therapeutic decision support, such as personalized anti-VEGF dosing regimens, predictive modeling of surgical outcomes, and dynamic adjustment of glaucoma management based on visual field forecasts. Integration of AI into practice through telemedicine platforms, electronic health record interoperability, and point-of-care screening is examined, along with results from prospective clinical validation studies. Finally, ethical, legal, and regulatory considerations—data privacy, algorithmic bias, liability frameworks, and the need for representative training datasets—are discussed. Looking ahead, this entry highlights emerging directions including multimodal data fusion, generative synthetic data, intraoperative AI guidance, and patient-centered AI interfaces. Overall, AI promises to augment ophthalmologists’ capabilities, improve diagnostic accuracy and efficiency, and ultimately enhance patient outcomes in vision care.