The study aims to develop proposed predictive formulas for determining the unconfined compression strength (UCS) of fly ash and cement stabilised clayey soil based on Multi-Gene Genetic Programming (MGGP) and Artificial Neural Network (ANN) techniques. Thirteen parameters, including the soil characteristics, the binder types, the binder contents, the curing period, the mixing method, and the fly ash characteristics, such as calcium oxide (CaO) content, CaO/SiO2 ratio, loss of ignition, were considered as the independent variables in the model. The results show that the selected optimal ANN and MGGP models can predict the target values with high correlation coefficients (R-value approximately 0.994 and 0.973, respectively), and low errors. The performances of the MGGP and ANN models were compared based on statistical parameters and several external criteria. The study finds that both models show their generalisation capabilities with robust, powerful, and accurate prediction ability; however, the ANN model slightly outperforms the MGGP model. The proposed predictive equations formulated from the selected optimal MGGP and ANN models could help engineers and consultants to choose the suitable binder and a reasonable amount of fly ash in the pre-planning and pre-design period.