Although marine controlled source electromagnetic (CSEM) methods are effective for investigating offshore freshened groundwater (OFG) systems, interpreting the spatial extent and salinity of OFG remains challenging. Integrating CSEM resistivity models with information on sub-surface properties, such as host-rock porosity, allows for estimates of pore-water salinity. However, deterministic inversion approaches pose challenges in quantitatively analyzing these estimates as they provide only one best-fit model with no associated estimate of model parameter uncertainty. To address this limitation, we employ a trans-dimensional Markov-Chain Monte-Carlo inversion on marine CSEM data, under the assumption of horizontal stratification, collected from the Canterbury Bight, New Zealand. We integrate the resulting posterior distributions of electrical resistivity with borehole and seismic reflection data to quantify pore-water salinity with uncertainty estimates. The results reveal a low-salinity groundwater body in the center of the survey area at varying depths, hosted by consecutive silty- and fine-sand layers approximately 20 to 60 km from the coast. These observations support the previous study’s results obtained through deterministic 2-D inversion and suggest freshening of the OFG body closer to the shore within a permeable, coarse-sand layer 40 to 150 m beneath the seafloor. This implies a potential active connection between the OFG body and the terrestrial groundwater system. We demonstrate how the Bayesian approach constrains the uncertainties in resistivity models and subsequently in pore-water salinity estimates. Our findings highlight the potential of Bayesian inversions in enhancing our understanding of OFG systems, providing crucial boundary conditions for hydrogeological modeling and sustainable water resource development.