This review discusses the role of artificial intelligence (AI) in improving glaucoma diagnosis through medical imaging. Glaucoma is a progressive, chronic, and often incurable disease that damages the optic nerve, leading to vision loss and blindness. Traditional diagnosis relies on ophthalmological exams, including fundus examination, tonometry, and visual field testing. However, early detection remains challenging due to the disease's silent progression and limited access to healthcare. AI technologies, particularly deep learning algorithms, are increasingly used to analyze digital fundus images for early glaucoma detection. AI methods, such as convolutional neural networks (CNNs), have shown promise in classifying glaucoma by analyzing features like the cup-to-disc ratio (CDR), ISNT rule, and optic disc damage. These algorithms can process large datasets and identify patterns that may be difficult for human experts to detect. Additionally, generative adversarial networks (GANs) are being explored to generate synthetic images for improving data diversity in training sets. The review highlights several public databases containing labeled fundus images for glaucoma classification, such as the Messidor, Origa-light, and PAPILA datasets. These databases are crucial for training and testing AI models. However, challenges remain, including limited data diversity, potential biases in labeling, and the need for more accurate diagnostic criteria. Despite these challenges, AI has the potential to significantly improve glaucoma diagnosis by enabling early detection, reducing underdiagnosis, and improving access to care. Future research should focus on developing more robust AI models, ensuring diverse and representative datasets, and integrating AI into clinical practice to enhance patient outcomes.This review discusses the role of artificial intelligence (AI) in improving glaucoma diagnosis through medical imaging. Glaucoma is a progressive, chronic, and often incurable disease that damages the optic nerve, leading to vision loss and blindness. Traditional diagnosis relies on ophthalmological exams, including fundus examination, tonometry, and visual field testing. However, early detection remains challenging due to the disease's silent progression and limited access to healthcare. AI technologies, particularly deep learning algorithms, are increasingly used to analyze digital fundus images for early glaucoma detection. AI methods, such as convolutional neural networks (CNNs), have shown promise in classifying glaucoma by analyzing features like the cup-to-disc ratio (CDR), ISNT rule, and optic disc damage. These algorithms can process large datasets and identify patterns that may be difficult for human experts to detect. Additionally, generative adversarial networks (GANs) are being explored to generate synthetic images for improving data diversity in training sets. The review highlights several public databases containing labeled fundus images for glaucoma classification, such as the Messidor, Origa-light, and PAPILA datasets. These databases are crucial for training and testing AI models. However, challenges remain, including limited data diversity, potential biases in labeling, and the need for more accurate diagnostic criteria. Despite these challenges, AI has the potential to significantly improve glaucoma diagnosis by enabling early detection, reducing underdiagnosis, and improving access to care. Future research should focus on developing more robust AI models, ensuring diverse and representative datasets, and integrating AI into clinical practice to enhance patient outcomes.