This chapter provides a detailed analysis of how artificial intelligence (AI) is transforming glaucoma diagnosis, monitoring, and treatment. It examines the use of machine learning (ML) and deep learning (DL) algorithms to detect early disease signs, evaluate optic nerve head (ONH) structure, analyze retinal nerve fiber layer (RNFL) thickness, and assess visual field (VF) changes. Both supervised and unsupervised learning models are discussed, including logistic regression, support vector machines (SVM), random forests, and neural networks, all showing high diagnostic accuracy in clinical studies. The chapter also explores deep learning frameworks such as convolutional neural networks (CNNs), generative adversarial networks (GANsGenerative adversarial networks (GANs)), and hybrid models that incorporate multimodal imaging data for comprehensive glaucoma evaluation. Beyond diagnosis, AI applications in progression detection and personalized treatment plans are highlighted, focusing on predictive modeling and clinical decision support. Together, these advancements highlight AI’s potential to enhance early glaucoma detection, improve treatment outcomes, and reduce the global burden of blindness.