Artificial Intelligence (AI) is transforming ophthalmology, particularly in the early diagnosis of retinal diseases. This review explores AI applications in various retinal conditions, including 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. AI, particularly through machine learning (ML) and deep learning (DL), offers enhanced screening efficiency, early diagnosis, and improved patient outcomes. AI models, such as IDx-DR, EyeArt, and Retmarker, have shown high accuracy in detecting and grading retinal diseases. These systems utilize image analysis, including fundus photography and optical coherence tomography (OCT), to identify disease markers and predict progression. However, challenges such as the "black box phenomenon," data biases, and limitations in comprehensive patient assessment remain. AI should complement, not replace, human expertise, ensuring a collaborative approach to healthcare. Future directions include improving AI generalizability, integrating multimodal imaging data, and developing interpretable models. AI has the potential to revolutionize retinal disease diagnosis, reduce healthcare disparities, and improve patient outcomes through enhanced diagnostic accuracy and personalized treatment strategies.Artificial Intelligence (AI) is transforming ophthalmology, particularly in the early diagnosis of retinal diseases. This review explores AI applications in various retinal conditions, including 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. AI, particularly through machine learning (ML) and deep learning (DL), offers enhanced screening efficiency, early diagnosis, and improved patient outcomes. AI models, such as IDx-DR, EyeArt, and Retmarker, have shown high accuracy in detecting and grading retinal diseases. These systems utilize image analysis, including fundus photography and optical coherence tomography (OCT), to identify disease markers and predict progression. However, challenges such as the "black box phenomenon," data biases, and limitations in comprehensive patient assessment remain. AI should complement, not replace, human expertise, ensuring a collaborative approach to healthcare. Future directions include improving AI generalizability, integrating multimodal imaging data, and developing interpretable models. AI has the potential to revolutionize retinal disease diagnosis, reduce healthcare disparities, and improve patient outcomes through enhanced diagnostic accuracy and personalized treatment strategies.