In this study, we develop a methodology to quantitatively integrate well resistivity logs and audio-magnetotelluric (AMT) sounding in the context of groundwater investigations. The experiments were conducted in a complex Quaternary depositional environment within a region of intensive groundwater use. A synthetic resistivity model was constructed from well resistivity log data and used for forward modelling to generate synthetic AMT responses, which were then inverted using Occam’s algorithm. The AMT-derived resistivity model was subsequently compared with the model obtained from the inversion of the field AMT sounding using the Pearson correlation coefficient (r) and the root mean square error (RMSE). A scalar shift factor (k) was introduced to optimize the match between both models. The comparison between the AMT-derived resistivity model and the well resistivity logs yielded a Pearson correlation coefficient of 0.669 and an RMSE of 0.1911. The optimal scalar shift factor was k = 1.1854, with a 95% confidence interval of [1.1470, 1.2246], indicating only a minor discrepancy. These results demonstrate that AMT can successfully recover a resistivity structure consistent with well resistivity logs. The proposed quantitative integration and validation workflow provides a robust framework to reduce the inherent ambiguity in AMT interpretation and highlights its potential as a direct hydrogeophysical tool for groundwater studies.