Abstract We have studied the regolith landform distribution in the area of Gaoua, western Burkina Faso, using an integration of geophysical and remote sensing data. Concentration maps of K, Th, U, as well as their ratios, were computed from airborne gamma-ray spectrometry data to assess the geochemical composition of the regolith. The mineralogy of the surfaces was mapped via the analysis of multispectral ASTER and Landsat scenes. Pauli-decomposition data retrieved from polarimetric ALOS PALSAR and Radarsat-2 images were included to characterize the surface properties of the regolith material. Morphometric variables such as slope, curvature, and relative relief were derived from the SRTM digital elevation model to quantify the topographic parameters of the different regolith landforms. An artificial neural network implementation, ADVANGEO, was then employed to extract four basic regolith landform units from the satellite and airborne data. Relic ferruginous duricrusts rich in hematite and goethite belonging to the High glacis, erosional surfaces represented by rock outcrops and suboutcrops, alluvial sediments, and soft pediment materials of the Middle and Low glacis were mapped successfully in the region. The results were compared with the existing geomorphological maps, an independent visual classification, and field observations. We found that the distribution and shape of the iron-rich duricrusts are more accurate than portrayed in the current maps. The best results, with an overall accuracy of 94.21% and a kappa value of 0.92, were obtained for a dataset consisting of gamma-ray spectrometry data combined with derivatives of the SRTM digital elevation model augmented by Landsat, and polarimetric radar data. The approach demonstrates for the first time the potential of machine learning in regolith landform mapping. The proposed combined analysis of airborne geophysics and remote sensing data can be adopted easily in other regions with similar long-term lateritic weathering histories worldwide.