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Detection of glaucoma has become critical, as it has arisen as the subsequent essential driver of visual impairment, around the world. At present, most of the algorithms in use rely on pre-trained deep neural networks to produce the best results. However, the high computational time and complexity and the need of a large database, make glaucoma-detection arduous and difficult. Keeping these in mind, this paper proposes a new convolutional neural network architecture, in particular, ProspectNet, which has demonstrated to accomplish a better accuracy with lesser computational time and complexity when tested against two pre-trained networks: VGG16 and DenseNet121. The data set is an amalgamation of two publicly available datasets- DRISHTI-GS and Glaucoma Dataset (Kaggle), comprising ocular colour fundus images of glaucomatous as well as normal eyes. ProspectNet has accomplished a normal AUC (area under the curve) as 0.991, specificity, and precision as 0.98. Confusion matrices also plotted to illustrate the new architecture’s efficacy. These outcomes demonstrate that ProspectNet is a hearty option in contrast to other best in class calculations for a medium sized dataset. The paper suggests three distinct structures for glaucoma detection. One advantage of our approach is that no special feature selection, such as detailed measurements of particular traits like the structure of the optic nerve head, is necessary.