Diabetic retinopathy is a condition resulting from harm to the veins and blood vessels of the retina. It might begin with little or no signs and progress to impaired or possibly absent vision. It is critical to get regular vision tests for diagnosis. Regulation of insulin levels and, in critical circumstances, the nerve that connects the eyes is placed in hazard by glaucoma that often occurs along with high levels of intraocular pressure. This may contribute to complaints including vision loss in the direction of vision. These medical conditions underscore the significance it is to undergo regular vision examinations in order to preserve eye health and promptly recognize and address any issues that arise. Skilled professionals must identify and interpret numerous minor anomalies. This study employs ResNetV3, VGG16, and CNN architectures to provide a unified deep neural networks strategy for DR identification. Current DR detection tools (Bogdănici et al. in Rom J Ophthalmol 62:112, 2017) [1] rely heavily on ophthalmologists for manual evaluation. To solve this issue, we developed a ResNetV3 network that diagnoses DR through virtual retinal images. Optimized ResNetV3 is designed to detect certain traits including color vision impairment, diabetic retinal detachment syndrome, glaucoma, and visual difficulties. This computerized system aims to increase diagnosis accuracy by offering rapid and effective answers with minimal human participation.