We developed a novel approach to predict pitting corrosion sites on 316L SS by combining RM with unsupervised ML techniques. By analyzing minor ( 0.5 %) variations in reflected light intensity during passive film formation, we identified areas prone to pit initiation. This established a deterministic link between the optical signatures of the passive layer and the specific locations where pits form in chloride-rich environments.