Abstract: The prediction of the soil–rock interface is crucial for airport construction in mountainous areas. Conventional methods typically rely on borehole data to directly estimate the elevation of the soil–rock interface. However, when topographic variability is pronounced and interface-elevation contrasts are large, the accuracy of such estimates is often insufficient to satisfy engineering requirements. This paper introduces an interpolation strategy that incorporates terrain elevation and interface depth. In this framework, the elevation of the soil–rock interface is expressed as the terrain elevation minus the depth of the interface. By shifting the prediction task to estimating interface depth alone, this approach reduces the influence of topographic variability on prediction accuracy. The performance of the proposed strategy is systematically evaluated using various prediction methods, including kernel methods such as Inverse Distance Weighting, Radial Basis Function Regression, and Gaussian Process Regression, as well as neural network approaches (e.g., Multilayer Perceptron and Kolmogorov–Arnold Networks). Case studies from airport projects in mountainous regions demonstrate that the strategy can be readily integrated with different prediction methods and substantially improves the accuracy of soil–rock interface predictions. The findings provide technical support for airport site selection, earthwork volume estimation, and construction planning in complex terrains.