Glaucoma is a progressive and often asymptomatic condition that leads to irreversible vision loss if left untreated, earning it the label of "the silent thief of sight." Slow disease progression often results in a delayed diagnosis. Treatment approaches for glaucoma range from pharmacological interventions to laser therapies and surgical procedures with the primary goal of reducing intraocular pressure (IOP). This review examines the current landscape of glaucoma diagnosis and treatment, highlighting the role of newer drug delivery techniques, advancements in laser and surgical options, and emerging use of artificial intelligence (AI) for enhanced patient outcomes. AI has made impressive progress in the field of glaucoma in recent years, with various machine learning models applied to diagnose glaucoma based on fundus photographs and optical coherence tomography, promising earlier detection and better patient management.

The prognosis of glaucoma is highly dependent on the stage at which it is detected, and the extent of optic nerve damage at the time of diagnosis. Early detection significantly improves the outcome, with proper treatment able to slow or halt the disease progression. The prognosis is generally favorable as long as the patients strictly adhere to their prescribed treatment regimen.

By analyzing both traditional and innovative therapeutic approaches, this study highlights the importance of personalized and targeted treatments to maximize the efficacy of glaucoma management. The incorporation of emerging technologies, particularly AI, represents a promising direction for improving the accuracy of diagnoses and enhancing future treatment strategies.