The detection of cryptic pollution, particularly coal mine drainage (CMD) neutralized by carbonate bedrock, remains a significant challenge in hydrogeology because these waters often retain toxic sulfate loads despite exhibiting a neutral pH. This automated remote sensing workflow identifies these anomalies without constant extensive ground sampling. We adapted the automated AMD detection method originally proposed by Rockwell et al. (2021) for the USGS, translating the workflow into custom Python algorithms for rapid processing of Landsat 8/9 and Sentinel-2 for higher resolution results. The method was modified to detect spectral anomalies specifically within water bodies, utilizing the iron sulfate index and new filtering equations to map potential pollution dispersion. Preliminary application to Ganau Pond (Kurdistan Region, Iraq), a site with high sulfate (>700 mg/L), revealed distinct spectral increases corresponding with suspected pollution inflows. Validated against ground truth geochemical data from the Muskingum Watershed, Ohio. This research presents a cost-effective, automated tool for preliminary water quality assessment in data-scarce regions. Presented at the 40th Annual Graduate Research Symposium, Kent State University.Visit My Website: https://www.climtawy.comCheck my Poster @ResearchGate: https://www.researchgate.net/publication/403521974_Automating_the_Detection_of_Cryptic_Sulfate_Pollution_Python_and_Machine_Learning_Implementation_for_Neutralized_Waters?utm_source=twitter&rgutm_meta1=eHNsLTc1cXZKeXF5YS84eFdZemFPUUllQS9DQ1JYL0o4OFF0TnlxelpNbFp6L0JRQ2tldXJLd3VRN2JQZ01xU2tvNStjcWMxSGpXUVVEdVRNSjFjMEl4QTJWUT0%3D