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This study presents novel intelligent modeling of the hydraulic conductivity (k) of sandy soil by employing machine learning (ML) algorithms i.e., artificial neural network (ANN), multi-expression programming (MEP) and genetic expression programming (GEP) on a large dataset. For this purpose, an extensive testing program was carried out to evaluate the k-value, gradation and compaction characteristics of a wide spectrum of sandy soils. A broad range of input parameters defining geological characteristics i.e., large, medium and small grain sizes (D), gradation parameters and dry density (γ_d) were engaged to resolve the limitation of existing k-value predictive models to cover output variability for the different combinations of D-value within a sandy soil deposit. Several possible models were generated by algorithm-guided iterations and varying algorithm inputs; thereof best models were scrutinized. The …