A group of eye conditions known as glaucoma impair the optic nerve, which is in charge of sending visual data from the eye to the brain. Glaucoma impacts 3.54% of adults aged 40 to 80 around the world. Early detection of glaucoma is crucial as it can prevent total optic nerve damage, which would cause irreversible vision loss. It is possible for specialists to diagnose glaucoma medically, but treatment options are either expensive or time-consuming and requires ongoing care from medical professionals. There have been numerous initiatives at streamlining all components of the glaucoma categorization process, however these models are challenging for users to comprehend the key predictors, resulting in them being unreliable for use by medical experts. The study uses eye fundus images to classify glaucoma patients using three distinct Deep Learning techniques: Convolutional neural network, Visual Geometry Group 16 (VGG16), and Global Context Network (GC-Net). In addition, several data pre-processing techniques are used to avoid overfitting and achieve high accuracy. This research compares and analyses the performance of various architectures using the aforementioned techniques. The CNN model had the best accuracy of 83% when in contrast to the other deep learning models.