According to anticipation, Diabetic Retinopathy (DR) is one among the most potential causes of visual disability and even blindness across the globe. Prompt diagnosis and treatment will stall the development towards irreversible damage. Hence, detection and staging of DR must play a huge role in early medical intervention and treatment planning. There exist tremendous challenges in accuracy, ability to capture retinal features, overfitting from poorly represented features, and inefficiencies in optimisation of parameters. These are likely to provide a very challenging situation for the clinical application of these methods, especially when dealing with huge heterogeneous datasets. The paper is discussing a new hybrid framework for detection and classification of DR that integrates the Swin-Transformer Integrated radial Residual Network (Swin-RadialNet) with a Butterfly-Mayfly Radial Optimizer (BMRO). This framework is intended to address those challenges. Hierarchical extraction of features from a model trained with radial residual connections atop the Swin Transformer architecture is carried out by Swin-RadialNet to guarantee the best learning of the complex k structures in retina on-the-fly through BMRO, which would act as an optimizer hybridizing to estimate radial spread parameters, thereby supporting acceleration of models' performance and converging rates. The core novelties of the proposed method shall lie in merging advanced transformer-based feature extraction with nature-inspired hybrid optimization to tackle efficiently critical issues regarding feature abstraction, parameter fine-tuning, and classification reliability. The method will be evaluated on several benchmark datasets such as Kaggle's Diabetic Retinopathy and APTOS 2019, and will expect to show state-of-the-art results for all DR stages in terms of accuracy, precision, recall, and F1-score.