Utilizing unsupervised ML algorithms like UMAP and K-means clustering allowed for autonomous detection of subtle patterns in the optical data without predefined training sets. This data-driven method successfully distinguished regions with varying susceptibility to pitting corrosion, highlighting potential chromium oxide (Cr 2 O 3 ) deficiencies in the passive layer. Optical modeling and ex-situ XPS supported the hypothesis that pit initiation sites correspond to zones with reduced chromium oxide content.