This paper provides a comprehensive review of both the progress and obstacles of AI techniques, specifically of machine learning (ML) and deep learning (DL). The author reviewed various studies, both clinical research and real-life applications, regarding how AI algorithms performed in ophthalmic diagnosis and diseases. It was highlighted that AI’s advancements have reached expert-level performance in the detection and classification of common ocular diseases, including diabetic retinopathy, glaucoma, and age-related macular degeneration, using imaging data like fundus photography and optical coherence tomography (OCT). Additionally, AI models have been reported to assist several areas of prognosis and surgical planning using validated algorithmic data of high sensitivity and specificity. In the discussion, the author also addressed the data privacy concerns and the need for larger datasets that prevent this technology from being generalized to the whole patient population. It seems that the necessity for standardization of AI framework and protocol needs to be explored more before effective integration of these systems in ophthalmic practice.