One of the main objectives of chemical stabilisation is to increase the compressive strength of soils. A wide range of parameters affect the strength improvement in cementitious stabilisation with chemicals. Accordingly, it is difficult to determine some kinds of functional relationships in strength improvement which make the precision of strength prediction to be satisfying. The purpose of the present study is to use two computational intelligence techniques namely, multilayer perceptron (MLP) and linear genetic programming (LGP), in order to develop the mathematical models to be capable of predicting the unconfined compressive strength. Subsequently, a comparison between these methods was performed in terms of prediction performance. Properties of natural soil such as textural properties, plasticity and linear shrinkage, stabiliser quantities and types (cement, lime, asphalt), for a wide range of soil types were used in order to generate the mathematical models to be able to predict the compressive strength as a quality of stabilised soil. A comprehensive set of data including 219 previously published stabilised unconfined compressive strength experimental determinations were utilised to develop the models.