Diagnosis and diagnosis of glaucoma progression remain difficult. Artificial intelligence-based techniques have the potential to improve and standardize glaucoma examination, however because of the multimodal and changeable nature of the diagnosis, developing these algorithms is difficult. Most algorithms are now focused on a single imaging modality, namely screening and diagnosis using fundus pictures or optical coherence tomography images. In our review of the literature, we found no research that evaluated the use of artificial intelligence for treatment response prediction, and no studies that conducted prospective testing of their algorithms. Another barrier to the development of artificial intelligence-based solutions is a lack of data and agreement on diagnostic criteria. Although research on the use of artificial intelligence for glaucoma is promising, more research is required to develop clinically usable tools. Current glaucoma detection convolutional neural networks (CNNs) are all based on spatial data embedded in an image. We created a hybrid CNN and recurrent neural network (RNN) that extracts not only the spatial but also the temporal characteristics encoded in a fundus image. A total of 1810 fundus photos and 295 fundus images were used to train a CNN and a CNN-LSTM-RNN combination. In differentiating glaucoma from healthy eyes, the combined CNN/RNN model achieved an average F-measure of 94.3%. A detecting system is required to aid in the early identification of glaucoma. The researchers propose employing deep learning technology to detect and forecast glaucoma before symptoms arise in this study. The results are contrasted with deep learning-based convolution neural network classification techniques. The suggested model has an accuracy of 98.21% when used for training and an accuracy of 96.34% when used for testing. According to all evaluations, the newly proposed paradigm is more effective than the one that is already in use.