The expanding field of artificial intelligence (AI) and machine learning (ML) in ophthalmology has affected for the better the way people predict and diagnose ocular diseases as well as stage the treatment. Thus, the present study looks to investigate how effective the AI algorithms and ML algorithms are in diagnosing diseases like diabetic retinopathy, age-related macular degeneration, and glaucoma. The purpose is to compare the performance metrics of these algorithms to those of traditional diagnostic approaches, particularly in terms of accuracy, sensitivity, and specificity. To begin with, diabetic retinopathy and age-related macular degeneration sensitivity rates improved, positively towards 90%, which is commendable. The improvement was statistically significant (p < 0.01) when the AI algorithm was included in the model. Particular attention is also drawn to the fact that the use of advanced diagnostic technologies is unlikely to be limited to the rationalization of diagnostic processes only, as they enable optimization of patient treatment management as well, thus relieving certain strains from the health care systems. In particular, this paper appreciates the unique value of AI and ML in ocular healthcare while providing a sound argument for their application in future studies and disease management