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This article provides a comprehensive review of deep learning-based blood vessel segmentation of the brain. Cerebrovascular disease develops when blood arteries in the brain are compromised, resulting in severe brain injuries such as ischemic stroke, brain hemorrhages, and many more. Early detection enables patients to obtain more effective treatment before becoming critically unwell. Due to the superior efficiency and accuracy compared to manual segmentation and other computer-assisted diagnosis procedures, deep learning algorithms have been extensively deployed in brain vascular segmentation. This study examined current articles on deep learning-based brain vascular segmentation, which examined the proposed methodologies, particularly the network architectures, and determined the model trend. We evaluated challenges and crucial factors associated with the application of deep learning to brain vascular segmentation, as well as future research prospects. This paper will assist researchers in developing more sophisticated and robust models in the future to develop deep learning solutions.