Abstract: The glymphatic system (GS) is a critical pathway for metabolic waste clearance in the central nervous system, with its structural damage and dysfunction closely associated with various neurological disorders. Conventional magnetic resonance imaging (MRI)-based evaluation of the GS relies on manual semi-quantitative measurements, which renders it unrepeatable and challenging to scale. Artificial intelligence (AI) enables automatic extraction and accurate quantification of MRI imaging features; thus, it has been rapidly adopted in GS imaging research in recent years. A total of 48 core Chinese and English studies published between 2023 and 2026 were retrieved in this study. We systematically reviewed research advances from three dimensions: intelligent segmentation of core GS structures, AI-aided quantitative analysis of functional imaging, and clinical translation of combined AI and GS imaging. Specifically, we horizontally compared the technical architectures and performance differences of mainstream segmentation models for perivascular spaces; summarized technical breakthroughs including automated diffusion tensor image analysis along the perivascular space, physics-informed deep learning, and multimodal fusion; and innovatively proposed a “structure-function” classification framework for GS injury. We further elaborated on the clinical value of AI-derived quantitative biomarkers in early disease screening, subtype differentiation, pathological mechanism exploration, and therapeutic efficacy assessment. Currently, this field is limited by insufficient cross-center generalizability and the absence of a gold standard for tiny lesions. Accordingly, we propose developmental suggestions and implementable strategies for short- and long-term stages, thus providing references for scientific research and the clinical translation of AI-assisted GS imaging.