Project Hydropower System Senj 2 (HPS Kosinj / HPP Senj 2) is planned to utilize the remaining hydropower potential of the Lika and Gacka watershed by upgrading the existing Senj hydropower system (HPS). As part of the HPP Senj 2, the construction of the Gusić polje 2 Compensation Basin is planned. It is formed by construction and reconstruction of about 3664,06 m long lateral embankments, active storage of 2.87 million m³ used for daily inflow regulation. The water side of the embankments and basin are waterproofed using the same techniques and the same materials – geomembrane. For this type of technical solution, it is important to calculate settlements of basin and embankments foundation soil. Over the years, geotechnical investigations, geophysics and exploratory boreholes, were carried out in the area. Spatial distribution and stratums thicknesses on karst landscapes is very difficult to estimate, and there is not one estimation technique that can resolve that task. In this paper, we examined application of machine learning to jointly interpret geophysical and borehole datasets for modelling subsurface spatial model of investigated area. Borehole data are used as hard and geophysical as soft data. Geophysical data are interpolated and borehole data associated these interpolated values. Furthermore we present machine learning models for classification using interpolated geophysical data, with basic model metrics. Classification models are applied to generate a 3D subsurface model of geotechnical stratums. Using 3Dsubsurface model, paired with mechanical properties, we can generate settlement map for the case of full reservoir. As well, the result is corresponding spatial uncertanty model, that highlights highest uncertanty areas that can be subject of further geotechnical inveatigation works. The proposed method proved to be suitable to jointly interpret database from common geotechnical investigation works in civil engineering practice. It is also suitable for identifying areas that need further investigation.