This work demonstrates that unsupervised machine learning can significantly enhance the capability of reflective microscopy to identify nanoscale heterogeneities in passive films on 316L stainless steel before visible pitting occurs. By applying K-means clustering to optical signatures, we successfully identified pitting-susceptible zones (PSZ) that strongly correlate with subsequent pit formation sites, with 82% of pit centroids located within these zones. This correlation was statistically validated across multiple experiments at varying NaCl concentrations.