This paper provides a comprehensive overview of the application of artificial intelligence (AI) in the early diagnosis and management of various retinal diseases. It highlights the potential of AI to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. The authors discuss the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their significance in ophthalmology. The paper delves into specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), macular neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), retinitis pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. It focuses on the current landscape of AI technologies, including various models, performance metrics, and clinical implications. The paper also addresses challenges and pitfalls associated with the integration of AI in clinical practice, such as the "black box phenomenon," biases in data representation, and limitations in comprehensive patient assessment. Finally, it emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery, where AI augments rather than replaces human expertise.This paper provides a comprehensive overview of the application of artificial intelligence (AI) in the early diagnosis and management of various retinal diseases. It highlights the potential of AI to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. The authors discuss the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their significance in ophthalmology. The paper delves into specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), macular neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), retinitis pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. It focuses on the current landscape of AI technologies, including various models, performance metrics, and clinical implications. The paper also addresses challenges and pitfalls associated with the integration of AI in clinical practice, such as the "black box phenomenon," biases in data representation, and limitations in comprehensive patient assessment. Finally, it emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery, where AI augments rather than replaces human expertise.