Glaucoma has been identified as a significant factor in the permanent loss of vision that occurs all around the world, according to certain reports. As a consequence of this, it is of the utmost importance to diagnose this disease at an early stage, in addition to providing an accurate diagnosis, in order to assist patients in receiving the appropriate treatment. However, in more recent years, deep learning, and more specifically convolutional neural networks, or CNNs, has exhibited promising findings in the field of medical image management, particularly in the diagnosis of glaucoma. This is particularly the case in the field of managing medical images. When it comes to improving the detection of glaucoma, which is a condition that affects the eyes, there is a revolutionary way that makes use of convolutional neural networks (CNNs). The research presented here demonstrates this alternative approach. After that, the effectiveness of this method is evaluated in comparison to that of the conventional strategy, which is known as the support vector machine (SVM) strategy. This evaluation is carried out in addition to the previous step. The purpose of this paper is to present a method for detecting glaucoma that entails training a CNN model with a dataset that is comprised of retinal pictures that have been specifically annotated. The following are some of CNN’s functionalities: The power to independently extract unique attributes from raw data image input is required in order to eliminate the requirement for manually designing features using algorithms. This is necessary in order to do away with the necessity to manually design features. The support vector machine (SVM) methodology is applied in this work for the purpose of comparison with the other approaches that were utilized before. The results of some of the research indicate that the CNN-based technique is superior to the SVM algorithm in terms of accuracy, sensitivity, and specificity when it comes to the detection of glaucoma. As a result of this, it has been proved that the CNN model is capable of functioning well as an automated screening tool. Because the retinal pictures that are displayed in the test comprise a variety of examples that do not relate glaucoma in any way, this is the reason why this is the case. Additionally, the research strives to identify the underlying aspects that contribute to the enhanced performance of CNN in order to better help the organization.