Glaucoma is a term used to describe either the progressive loss of retinal cells within the optic nerve or the gradual visual impairment caused by optic neuropathy. Glaucoma is a disease that affects vision in the eye. It is considered an irreversible condition that leads to vision deterioration. There are no early warning signs for glaucoma, and changes in vision may go unnoticed due to their subtle nature. Currently, numerous deep learning (DL) models have been developed for accurate glaucoma diagnosis. Therefore, we propose an architecture for precise glaucoma detection using Convolutional Neural Networks (CNNs). CNNs can distinguish between patterns specific to glaucomatous and non-glaucomatous conditions, providing hierarchical features for distinction in images. In the approach, glaucoma diagnosis hinges on the optic cup-to-disc ratio. The integration of an image data augmentation method enhances diagnostic accuracy. The results indicate that our proposed model, surpassing several existing algorithms, achieved an accuracy of 98%.