Abstract— Deep learning has emerged as a transformative technology in ophthalmology, addressing critical challenges in the diagnosis and management of eye diseases such as diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), and central serous chorioretinopathy (CSCR). These conditions, among the leading causes of preventable blindness, require accurate and timely detection, which is often limited by traditional diagnostic methods due to inefficiency and the complexity of interpretation. The goal of this study is to examine the applications of deep learning in the diagnosis of ophthalmic diseases and to help researchers gain a better understanding of recent advances in model development, identify challenges associated with widespread implementation of these models in real-world applications, and outline future research directions in this area. Methodologically, recent studies using convolutional neural networks (CNNs), vision transformers, and hybrid models demonstrate high diagnostic accuracy and potential for early disease detection. Applications extend beyond disease diagnosis to lesion segmentation, disease progression monitoring, and personalized treatment planning. Deep learning systems have demonstrated comparable or superior diagnostic performance to human experts in detecting diseases such as DR and glaucoma. Despite these advances, challenges remain, including limited generalizability, data bias, and the need for explainable AI models to foster clinical trust and adoption. Addressing these challenges through improved model transparency, diverse datasets, and ethical frameworks will be critical to integrating deep learning into routine ophthalmic practice. This review highlights the significant advances in deep learning-driven ophthalmology and outlines a path for future research to optimize its clinical implementation.