In this context, the objective of this work was to enhance detection capabilities of RM with unsupervised ML methods to identify pitting-susceptible zones (PSZ) on stainless steel substrates. The optical signatures extracted through unsupervised ML techniques were rationalized using optical modeling and complementary ex-situ X-ray photoelectron spectroscopy (XPS) surface analysis. We demonstrate this approach through a case study involving SS 316L alloy in NaCl electrolyte environments under anodic polarization and explore future directions for the development of predictive electrochemical corrosion monitoring technologies based on these results.