In　response　to　the　growing　number　of　diabetes　cases　worldwide,　Our　study　addresses　the　escalating　issue　of　diabetic　eye　disease　(DED),　a　significant　contributor　to　vision　loss　globally,　through　a　pioneering　approach.　We　propose　a　novel　integration　of　a　Genetic　Grey　Wolf　Optimization　(G-GWO)　algorithm　with　a　Fully　Convolutional　Encoder-Decoder　Network　(FCEDN),　further　enhanced　by　a　Kernel　Extreme　Learning　Machine　(KELM)　for　refined　image　segmentation　and　disease　classification.　This　innovative　combination　leverages　the　genetic　algorithm　and　grey　wolf　optimization　to　boost　the　FCEDN＇s　efficiency,　enabling　precise　detection　of　DED　stages　and　differentiation　among　disease　types.　Tested　across　diverse　datasets,　including　IDRiD,　DR-HAGIS,　and　ODIR,　our　model　showcased　superior　performance,　achieving　classification　accuracies　between　98.5%　to　98.8%,　surpassing　existing　methods.　This　advancement　sets　a　new　standard　in　DED　detection　and　offers　significant　potential　for　automating　fundus　image　analysis,　reducing　reliance　on　manual　examination,　and　improving　patient　care　efficiency.　Our　findings　are　crucial　to　enhancing　diagnostic　accuracy　and　patient　outcomes　in　DED　management.　　©　2024　Khan　et　al.　This　is　an　open　access　article　distributed　under　the　terms　of　the　Creative　Commons　Attribution　License,　which　permits　unrestricted　use,　distribution,　and　reproduction　in　any　medium,　provided　the　original　author　and　source　are　credited.