Delineatingand mapping the bedrockand overlaid deposits due to 8complex spatial patterns, associated uncertainties and sparse data is a vital diffi-9cult task in geo-engineering applications. Modern computing techniques such as 10artificial intelligence-based models (AIM) are appropriate alternative to over-11come the deficiencies of previous methods. The objective of this study is to in-12vestigatethe feasibility of AIM in prediction of 3D spatial distribution of subsur-13face bedrock in a large area in Stockholm, Sweden. The predictive artificial in-14telligence models were developed using 1968 processed soil-rock soundings 15comprising the geographical coordinates and ground surface elevation. The opti-16mum topology was captured through the examining of wide variety of internal 17characteristics.It was observed that in sparse dataset, the developed AIMs effi-18ciently can provide much more accurate prediction than traditionally applied 19techniques such as geostatistical approaches. This implies that the developed 20AIM due to significant capacities and acceptable predictability level can decrease 21the residuals between the predicted and measured data.