Glaucoma　is　a　progressive　eye　disease　that　can　lead　to　blindness　if　left　untreated.　Early　detection　is　crucial　to　prevent　vision　loss,　but　current　manual　scanning　methods　are　expensive,　time-consuming,　and　require　specialized　expertise.　This　study　presents　a　novel　approach　to　Glaucoma　detection　using　the　Enhanced　Grey　Wolf　Optimized　Support　Vector　Machine　(EGWO-SVM)　method.　The　proposed　method　involves　preprocessing　steps　such　as　removing　image　noise　using　the　adaptive　median　filter　(AMF)　and　feature　extraction　using　the　previously　processed　speeded-up　robust　feature　(SURF),　histogram　of　oriented　gradients　(HOG),　and　Global　features.　The　enhanced　Grey　Wolf　Optimization　(GWO)　technique　is　then　employed　with　SVM　for　classification.　To　evaluate　the　proposed　method,　we　used　the　online　retinal　images　for　glaucoma　analysis　(ORIGA)　database,　and　it　achieved　high　accuracy,　sensitivity,　and　specificity　rates　of　94%,　92%,　and　92%,　respectively.　The　results　demonstrate　that　the　proposed　method　outperforms　other　current　algorithms　in　detecting　the　presence　or　absence　of　Glaucoma.　This　study　provides　a　novel　and　effective　approach　to　Glaucoma　detection　that　can　potentially　improve　the　detection　process　and　outcomes.