Abstract: This paper pivots on (DR) is a leading cause of blindness worldwide. Early detection and effective management are crucial in preventing damage to the retina. In recent years, (DL) techniques have shown promise in aiding the DR. This paper aims to investigate the diagnosis of using (CNNs) for automated DR screening. The study utilized a available patient dataset of retinal images to train and testing CNN model for DR detection and classification. Results showed that given (DL) techniques achieved with accurately in both tasks, outperforming existing state-of-the-art methods. The findings suggest the (DL) algorithms can aid in the early detection and management of DR, potentially reducing the burden of the condition on healthcare systems and improving patient outcomes