<p>Diabetic retinopathy (DR) is a leading cause of vision impairment and blindness globally, with its severity classified into non-proliferative and proliferative stages. Effective detection and segmentation of multiple lesions are crucial for accurate diagnosis and management. This study presents a novel fuzzy logic-based model for the segmentation of multi-lesion diabetic retinopathy, which addresses the complexities associated with varying severity levels. The proposed multi-phase approach employs a comprehensive fuzzy inference system to enhance the detection and classification of diverse lesions related to diabetic retinopathy. Evaluated using three benchmark datasets—Messidor-2, Eyepacs, and IDRiD—proposed model achieved an accuracy of 99.57% on the Messidor-2 dataset and consistently high precision, recall, and F1-scores exceeding 99% across all datasets. Comparative analyses reveal that the proposed model significantly outperforms existing approaches, with an accuracy increase of 2.45% in multi-lesion segmentation metrics. Additionally, statistical validation demonstrates the robustness of our findings, with p-values less than 0.001. This study emphasizes the potential of fuzzy logic in enhancing diagnostic capabilities for diabetic retinopathy through precise lesion segmentation, ultimately aiding in improved patient outcomes.</p>