Diabetic retinopathy is a major blindness causing disease that occurs by affecting the retina of the eye. It is caused by diabetes due to high blood sugar levels. To address this challenge, an Automated Diabetic Retinopathy Detection System is built that utilizes deep learning techniques. The system employs a deep learning model based on the CNN architecture, which has been trained on a different class of labeled dataset of retinal images. By categorizing images into different stages of diabetic retinopathy, ranging from Healthy to Proliferative DR, the model enables early detection. The user-friendly interface of the system allows for easy submission of retinal images, providing precise predictions and confidence scores. This technological aspect of the system, its ability to categorize diabetic retinopathy stages, and the empowerment it offers to patients and healthcare providers in managing vision health.