Diabetic retinopathy (DR) is a severe consequence of diabetes and a major cause of vision impairment globally, particularly affecting individuals in their lifetime. Early detection and timely treatment can significantly prevent vision loss in many individuals with DR. Once DR symptoms are identified, the disease’s severity can be assessed to determine the most suitable course of treatment. This manuscript focuses on classifying DR from fundus images based on its severity level by using ResNet, MobileNet, GoogLeNet, and VGG16. These models are considered for analysis due to their proven effectiveness in image classification tasks, robustness in feature extraction, and efficiency in handling medical imaging datasets. ResNet’s deep residual connections provides detailed information, whereas MobileNet’s lightweight architecture optimizes speed, GoogLeNet’s inception module, and VGG16’s simple convolutional layers make them well-suited for DR classification. These models are trained by experimentally determined learning rate, optimizer, and loss function to achieve higher accuracy. These models have been tested using the APTOS 2019 dataset consisting of 5593 retinal images across 5 classes and obtained an overall accuracy of 95.89%.