A continuous on-site, in situ system can estimate lead (Pb) concentration in municipal water . The system leverages the SVR algorithm, supplanting traditional mathematical models confined to analyzing stationary ions in a solid substrate. By using the radio-frequency reflection coefficient of the raw trace data, the system predicts Pb ion concentration with a resolution of 1 μg Pb/L and an RMS prediction error of 0.71 μg Pb/L in the presence of interfering metals such as copper (Cu²⁺), ferric (Fe³⁺), and zinc (Zn²⁺) ions. Other than estimating heavy metal concentration in individual samples, ML is promising in broader analytical applications. For instance, ML is useful in spatially interpolating environmental variables, significantly enhancing its performance . This approach is valid in developing spatial interpolation maps depicting the concentrations of heavy metals such as Fe, Mn, Ni, Pb, and Zn in groundwater (i.e., a source of drinking water) . That study combined ML and geostatistical interpolation (MLGI) to leverage an ANN-based algorithm to augment the efficacy and robustness of the spatial interpolation mapping. Furthermore, the MLGI approach comprehensively assesses the carcinogenic risks of heavy metals through in situ measurements. The approach produces detailed spatial maps delineating heavy metal concentration and estimates health quotient indices (HQI) to offer a more refined risk assessment .