The prevalence of retinal diseases poses a significant threat to global eye health, necessitating efficient and timely diagnostic methods. This study explores the application of deep learning algorithms in detecting and classifying retinal diseases from medical imaging data. Leveraging a diverse dataset of retinal scans, our approach employs convolutional neural networks (CNNs) to extract intricate features crucial for accurate diagnosis automatically. The proposed deep learning model undergoes extensive training on a labeled dataset comprising images of various retinal conditions, including diabetic retinopathy, macular degeneration, and glaucoma. The trained model demonstrates remarkable performance in distinguishing between diseases, showcasing its potential as a robust diagnostic tool.