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Abstract: Diabetic Retinopathy (DR) is a severe complication of diabetes that can lead to vision loss if not detected early. Early diagnosis and treatment are crucial for preventing blindness. This study explores the application of deep learning techniques for the automated classification of diabetic retinopathy from retinal images. We employed convolutional neural networks (CNNs) to analyze a large dataset of retinal images, aiming to accurately the severity of DR. The dataset was pre-processed to enhance image quality and ensure consistent input for the model. The results demonstrate that the deep learning approach achieved high accuracy, sensitivity, and outperforming conventional methods. This study highlights the potential of deep learning to enhance early detection and facilitate timely intervention for diabetic retinopathy, ultimately improving patient outcomes and reducing the burden on healthcare systems.