Cerebral small vessel disease (CSVD) is the primary cause of ischemic stroke and vascular cognitive impairment, and its occurrence and development are closely related to multi-scale hemodynamic dysfunction from intracranial large arteries to distal microcirculation. However, it is difficult for traditional imaging assessment methods to achieve precise, non-invasive, and integrated quantitative analysis of this complete pathway. In recent years, the rapid development of artificial intelligence (AI) technology has provided innovative tools to solve this problem. This article systematically reviews the research progress and clinical translation status of AI-empowered hemodynamic assessment of CSVD. First, we elaborate on the technical support role of AI in the pre-processing steps such as automated segmentation, noise reduction, and registration of multimodal imaging data. Then, we focus on the application progress of AI in the precise quantification of key parameters such as intracranial large artery blood flow velocity and wall shear stress, cerebral microcirculation perfusion heterogeneity, cerebral autoregulatory function, three-dimensional morphology of perforating arteries, and blood-brain barrier permeability. Finally, we summarize the predictive value of AI through multi-scale information fusion in the subtype differentiation of CSVD, cognitive decline, and risk stratification of disease progression. Based on this, we further explore the current core bottlenecks in clinical translation, including: the lack of structured human-factor usability verification for explainable AI technology, the potential for automated biases triggered by post hoc explanations, the mismatch between the traditional evidence-based medical framework and the dynamic evolving AI system paradigm, and the discontinuity of the evidence chain of health technology assessment. Future breakthroughs need to focus on constructing a "surrogate endpoint - intermediate indicator - hard endpoint" hierarchical verification path, placing human-factor verification prior to the AI model development process, and establishing a dynamic regulatory assessment system covering the entire life cycle.