Artificial intelligence (AI) is used in brain arteriovenous malformation (AVM) neuroimaging, but translation readiness remains incompletely mapped.

To characterize AI-related applications, reported performance, and barriers to clinical deployment.

We conducted a PRISMA-ScR-guided review of PubMed, Embase, and Google Scholar from inception through 20 December 2025, with PubMed additionally searched on 23 August 2026. Eligible sources addressed AI, machine learning, radiomics, or automated computational analysis of brain AVM imaging. Results were charted descriptively; heterogeneous metrics were not pooled.

Thirty-two sources were included: 28 primary reports and four contextual evidence syntheses. Thirteen primary reports had a documented AI/ML component. Of these, seven used imaging-derived inputs only, three combined imaging with clinical or treatment predictors, one compared separate imaging and clinical models, and two had unresolved final predictor sets. Three nidus-segmentation reports gave Dice estimates of 0.754, 0.80 and 0.82; partition details were unavailable for the first, and the last applied to detected lesions. Validation designs and clinical targets varied, precluding a pooled performance estimate.

AI/ML shows technical feasibility in selected brain AVM imaging and prediction tasks. Clinical benefit and routine deployment have not been demonstrated. Multicenter institutional validation, calibration, transparent reporting and prospective clinical-impact evaluation remain priorities.