The impact of urban wet weather discharges (UWWD) from combined sewer overflows on water quality is a major environmental concern and has been associated with an increase in ecotoxic responses from bioassays performed as part of a monitoring program of the Seine River. UWWD contain a diversity of micropollutants that can originate from both runoff and wastewater overflow, and thus are difficult to characterize. In this study, a non-targeted approach using high-resolution mass spectrometry (HRMS) was employed to characterize the presence of UWWD contaminants in surface water. Various amounts of UWWD were added to river water samples and analyses were performed by ultraperformance liquid chromatography coupled to ion mobility spectrometry and QTOF detection, after solid-phase extraction. Statistical analyses were performed on the HRMS signals to first determine the smallest amount of UWWD that would cause a significant difference to the HRMS signals of the surface water. Both supervised and unsupervised analysis techniques were then used to develop two types of predictive models: regression models designed to predict the proportion of UWWD in a water sample, and classification models designed to distinguish whether the sample is lightly or heavily contaminated with UWWD. This methodology is an important step towards environmental monitoring and water quality management, especially during periods of intense rainfall. The developed predictive models could be used to efficiently assess the presence and amount of UWWD in surface water, without targeting specific contaminants. Furthermore, the results of this study pave the way for an integration of these approaches with ecotoxicological analyses for a more comprehensive assessment of the environmental impact of UWWD.