This chapter explores the transformative role of artificial intelligence (AI) in glaucoma diagnosis and management, focusing on its ability to analyze complex structural and functional data for early detection and progression monitoring. Techniques such as convolutional neural networks (CNNs) have advanced the interpretation of imaging modalities like fundus photography and optical coherence tomography (OCT), as well as functional assessments like visual field tests. Emerging applications include automated disease classification, structure–function mapping, and natural language processing (NLP) for leveraging electronic health records. The chapter also addresses challenges, including data integration, validation, and generalization, while highlighting AI’s potential to improve clinical decision-making and patient outcomes in glaucoma care.