In this paper, test loop experiments and machine learning techniques were combined to investigate pressure drops of fresh cemented paste backfill (CPB) mixes. The influence of tailings characteristics on CPB pressure drops was studied and Extensive test loop experiments were performed. The complex mapping from tailings characteristics, cement-tailings ratio, inlet velocity and solids content to pressure drop was successfully learned by decision tree regression (DTR) models. In addition, the influence of training set size and maximum tree depth on DTR performance was investigated. The relative importance of predictor variables was discussed and the visualisation for a representative DTR model was provided. Finally, the current research applied two ensemble techniques, namely random forest and gradient boosting regression tree, to increase the predictive performance of the DTR models. The study found that all ensemble techniques outperformed DTR in the pressure drop prediction of CPB.