As cemented paste backfill (CPB) plays an increasingly important role in minerals engineering, forecasting its mechanical properties becomes a necessity for efficient CPB design. Machine learning (ML) techniques have previously demonstrated remarkable successes in such task by providing black-box predictions. To express the non-linear relationship in an explicit and precise way, we employed genetic programming (GP) for the uniaxial compressive strength (UCS) prediction of CPB. The influence of sampling method, training set size and maximum tree depth on the GP performance was investigated. A detailed analysis was conducted on a representative GP model and the relative variable importance was investigated using the relative variable frequency, partial dependence plots and relative importance scores. The statistical parameters show that a satisfactory performance was obtained by the GP modelling (R 2 > 0.80 on the testing set). Results of this study indicate that cement-tailings ratio, solids content and curing time were the most three important variables for the UCS prediction. The predictive performance of GP modelling was comparable to well-recognised ML techniques, and the trained GP model can be generalised to entirely new tailings with satisfactory performance. This study indicates that the GP-based method is capable of providing explicit and precise forecasting of UCS, which can serve as a reliable tool for quick, inexpensive and effective assessment of UCS in the absence of adequate experimental data.