Defacing of brain magnetic resonance imaging (MRI) scans is a crucial process in medical imaging research aimed at preserving patient privacy while maintaining data integrity. However, existing defacing algorithms are prone to errors, potentially compromising patient anonymity. This paper investigates the feasibility and efficacy of automated quality assessment for defaced brain MRIs using machine learning (ML). Our findings demonstrate the promising capability of ML models in accurately distinguishing between properly and inadequately defaced MRI scans.