Given limitations in data, particularly in field data, employing classification over direct regression for relative risk assessment is more robust. That study used a binary classification framework, labeling relative risk scores above the median as 1 for relatively high risks and scores below the median as 0 for relatively low risks. Compared with LR and DT, RF has the highest accuracy (0.86), precision (1.0), recall (0.75), F1 (0.86), and AUC (0.88). Finally, the feature importance analysis from RF reveals that green (areas designated for natural vegetation, parks, forests, or other green spaces), DO, and pH are the top-three significant influencing factors of AMR in drinking water.