Abstract: The ensemble Kalman filter (EnKF) is one of the most widely used data assimilation methods. However, it exhibits limitations in handling non-Gaussian problems. To effectively address such issues and accurately describe the connectivity of aquifers, a novel approach named NS-ES-MDA is developed in this study. The proposed NS-ES-MDA synergistically combines the normal-score transformation (NST) with ensemble smoother with multiple data assimilation (ES-MDA). Through comparative experiments, the efficacy of NS-ES-MDA in estimating the hydraulic conductivity of non-Gaussian distributed aquifers is demonstrated. By assimilating the same dataset, NS-ES-MDA exhibits approximately 34% improvement in parameter estimation accuracy and about 35% enhancement in computational efficiency compared to the restart normal-score ensemble Kalman filter (rNS-EnKF). Furthermore, the NS-ES-MDA shows case robustness against the “equifinality” and displays remarkable updating capabilities, which leads to more precise parameter estimates. This study provides an effective solution for parameter estimation in non-Gaussian distributed aquifers.