This paper investigates the application of EfficientNetV2, an advanced variant of EfficientNet, in diabetic retinopathy (DR) detection, a critical area in medical image analysis. Despite the extensive use of deep learning models in this domain, EfficientNetV2’s potential remains largely unexplored. The study conducts comprehensive experiments, comparing EfficientNetV2 with established models like AlexNet, GoogleNet, and various ResNet architectures. A dataset of 3662 images was used to train the models. Results indicate that EfficientNetV2 achieves competitive performance, particularly excelling in sensitivity, a crucial metric in medical image classification. With a high area under the curve (AUC) value of 98.16%, EfficientNetV2 demonstrates robust discriminatory ability. These findings underscore its potential as an effective tool for DR diagnosis, suggesting broader applicability in medical image analysis. Moreover, EfficientNetV2 contains more layers than AlexNet, GoogleNet, and ResNet architecture, which makes EfficientNetV2 the superior deep learning model for DR detection. Future research could focus on optimizing the model for specific clinical contexts and validating its real-world effectiveness through large-scale clinical trials.