In this study, we advance opto-electrochemical microscopy for pitting corrosion by integrating machine learning for image processing alongside recent advancements in optical modeling and instrumentation. The essence of our innovation lies in the principle that variations in the intensity of reflected light can be quantitatively linked to the thickness of surface films, akin to the spatially resolved ellipsometry method, but with the benefit of a more straightforward experimental setup, termed Reflective Microscopy (RM). In our prior studies, we have already demonstrated the capability of RM to estimate surface film thickness with precision down to a monolayer that is comparable to that of Transmission Electron Microscopy (TEM), STM, AFM, and XRD using synchrotron radiation, among other advanced analytical techniques. Notably, RM is considerably more cost-effective and timeefficient, as it rapidly generates large amounts of data-capturing up to 50 frames per second with a pixel resolution down to hundreds of nanometers. The high-throughput capability makes it well-suited for integration with predictive machine learning (ML) approaches. The objective of this work was to establish a deterministic link between the properties of the preformed passive layer, based on its optical signature, and the specific locations where pits form due to the breakdown of this layer as corrosion advances. Initially, this was approached phenomenologically using data-driven correlations through ML. In our previous works and in this study, we advocate for the use of unsupervised approaches, as they enable the discovery of hidden numerical patterns that would otherwise remain unnoticed, without requiring predefined training data and operating in a fully autonomous manner. The physical origin of the identified correlations was further explored through optical modeling and complementary ex-situ X-ray photoelectron spectroscopy (XPS) surface analysis. We illustrate this approach with a case study involving SS 316L alloy in NaCl environments under anodic polarization and explore future directions for the development of predictive corrosion monitoring technologies informed by these results.