Diabetic Retinopathy (DR), a leading cause of blindness and visual impairment, arises from prolonged diabetes mellitus with poor glycemic control, leading to structural damage in the retina. DR is becoming a critical medical challenge, affecting individuals’ vision and overall health. While ophthalmologists can manually diagnose DR, this approach is labor-intensive and time-consuming, particularly in today’s high demand clinical environments. Early detection and prevention of DR require an automated, precise, and personalized approach using deep learning. Various deep learning techniques have been explored for DR severity classification, with Convolutional Neural Networks (CNNs) being the predominant choice. However, CNNs have limitations in capturing long-range dependencies within retinal images. Recently, transformers have gained prominence in computer vision, demonstrating superior performance in natural language processing. Transformers utilize multi-head self-attention mechanisms to model complex contextual interactions between image pixels, addressing the shortcomings of CNNs. This study proposes a transformer-based approach for DR classification, leveraging its self-attention mechanisms to enhance feature extraction and improve diagnostic accuracy. Fundus images are segmented into non overlapping patches, which are then flattened into sequences and processed through a linear projection and positional embedding technique to retain spatial information. These sequences are subsequently fed into multiple layers of transformer attention mechanisms to generate the final feature representation. In practical clinical applications, transformer-based models can provide ophthalmologists with rapid, precise, and individualized diagnostic insights, facilitating timely medical interventions and improving patient outcomes.