This study introduces an optical approach enhanced by unsupervised machine learning (ML) to identify pitting-susceptible zones on stainless steel 316L, even from low signal-to-noise ratios. Analysis suggested chromium oxide deficiency in these zones, consistent with known pitting mechanisms. This proof-of-concept demonstrates that ML-enhanced optical methods offer accessible, precise tools for predictive corrosion monitoring.