Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual diferences involved in this process have not been thoroughly studied. We used machine learning to examine the anatomical profle of the cerebral arterial tree. The method is less sensitive to inter-subject and cohort-wise anatomical variations and exhibits robust performance with an unprecedented in-depth vessel range. We applied machine learning algorithms to disease-free healthy control subjects (n= 42), patients with stroke with intracranial atherosclerosis (ICAS) (n= 46), and patients with stroke mixed with the existing controls (n= 69). We trained and tested 70% and 30% of each study cohort, respectively, incorporating spatial coordinates and geometric vessel feature vectors. Cerebral arterial images were analyzed based on the ‘segmentation-stacking’ method using magnetic resonance angiography. We precisely classifed the cerebral arteries across the exhaustive scope of vessel components using advanced geometric characterization, redefnition of vessel unit conception, and post-processing algorithms. We verifed that the neural network ensemble, with multiple joint models as the combined predictor, classifed all vessel component types independent of inter-subject variations in cerebral arterial anatomy. The validity of the categorization performance of the model was tested, considering the control, ICAS, and control-blended stroke cohorts, using the area under the receiver operating characteristic (ROC) curve and precision-recall curve. The classifcation accuracy rarely fell outside each image’s 90–99% scope, independent of cohort-dependent cerebrovascular structural variations. The classifcation ensemble was calibrated with high overall area rates under the ROC curve of 0.99–1.00 [0.97–1.00] in the test set across various study cohorts. Identifying an all-inclusive range of vessel components across controls, ICAS, and stroke patients, the accuracy rates of the prediction were: internal carotid arteries, 91–100%; middle cerebral arteries, 82–98%; anterior cerebral arteries, 88–100%; posterior cerebral arteries, 87–100%; and collections of superior, anterior inferior, and posterior inferior cerebellar arteries, 90–99% in the chunk-level classifcation. Using a voting algorithm on the queued classifed vessel factors and anatomically post-processing the automatically classifed results intensifed quantitative prediction performance. We employed stochastic clustering and deep neural network ensembles. Ma-chine intelligence-assisted prediction of vessel structure allowed us