Guaranteeing clean drinking water to the global population is becoming more challenging, because of the cases of water scarcity across the globe, growing population, and increased chemical footprint of this population. Existing targeted strategies for hazard monitoring in drinking water are not adequate to handle such diverse and multidimensional stressors. In the current study, we have developed, validated, and tested a machine learning algorithm based on the data produced via non-targeted liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS) for the identication of potential chemical hazards in drinking water. The machine learning algorithm consisted of a composite statistical model including an unsupervised