Currently, the early diagnosis of glaucoma and monitoring of disease progression is difficult and requires assessment of structural(fundus photo/ optical coherence tomography scan)and functional damage(visual fields)of the optic nerve head(ONH). It requires the clinical knowledge of glaucoma experts and is highly labor intensive. Artificial intelligence(AI)applications have been proposed to improve the understanding of glaucoma and help to reduce the time and manpower required for such clinical tasks. With the advent of deep learning(DL), many tools for ophthalmological image enhancement, segmentation and classification have also emerged. Especially in the last three years, a large number of algorithms suitable for analyzing the ONH structure and/or function, which have been proposed to help in glaucoma detection. AI tools have also been developed to predict the early progression of the disease. Bring the possibility of personalized precision treatment. However, these algorithms are yet to be tested in the real world. This review summarizes the diverse landscape of AI algorithms developed for glaucoma. We also discuss the current limitations and challenges that we need to overcome.