In acute ischemic stroke, collateral circulation plays a critical role in maintaining ischemic penumbral perfusion, delaying infarct progression, and improving outcomes after endovascular treatment (EVT). Currently, collateral assessment mostly relies on computed tomography angiography (CTA), multiphase CTA, computed tomography perfusion, magnetic resonance perfusion, or digital subtraction angiography, and still mainly depends on visual grading systems such as the Tan, Maas, and American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology scores. These approaches are subjective and show limited reproducibility, failing to meet the clinical demand for rapid and objective quantitative evaluation. In the context of increasingly refined reperfusion strategies, accurate evaluation of collateral capacity is essential for explaining heterogeneity in infarct progression and the futile recanalization, defined as successful recanalization without effective tissue reperfusion. In recent years, deep learning methods have been applied to enable automated quantitative assessment of collateral circulation. Existing studies have focused on automated collateral grading, extraction and multiscale quantification of vascular structure, and multimodal predictive models integrating perfusion parameters with clinical information. Despite encouraging progress, challenges remain, including limited data availability, class imbalance, domain shift, and the absence of unified grading standards. This review summarizes recent advances and key bottlenecks of deep learning in the assessment of collateral circulation in acute ischemic stroke and discusses future directions, including multicenter standardization, dynamic blood flow modeling, self-supervised learning, explainable artificial intelligence, and integration into clinical workflows, so as to facilitate more robust and generalizable collateral assessment frameworks.