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Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. At present to detect Glaucoma intraocular pressure (IOP) method is used. It is a fluid pressure inside the eye. The intraocular pressure (IOP) measurement uses tonometry, which sometimes may increases the pressure due to which optic nerve is damaged. Optic nerve head assessment in retinal fundus images is both more promising and superior. This method uses 3D fundus images. 3D images are not easily available and of high cost. So to avoid these problems glaucoma screening using superpixel classification is used. This research proposes optic disc and optic cup segmentation using superpixel classification for glaucoma screening. It uses the 2D fundus images. In optic disc segmentation, clustering algorithms are used to classify each superpixel as disc or non-disc. The Cup to Disc Ratio (CDR) of the color retinal fundus camera image is the primary identifier to confirm Glaucoma for a given patient.