Depletion of river sand due to large-scale concrete production has caused many environmental problems. To address this issue, river sand can be replaced with sand manufactured from waste deposits. To facilitate manufactured-sand concrete production, this study proposes three tree-based models: one individual model (regression tree (RT)), and two ensemble models (random forest (RF) and gradient boosted regression tree (GBRT)) to predict its mechanical properties, such as uniaxial compressive strength (UCS), and splitting tensile strength (STS). These tree-based models were trained and tested on a dataset collected from previous literature. In addition, to understand the importance of each input variable on the mechanical properties of manufactured-sand concrete, the variable importance is calculated using the RF algorithm. The results show that the highest correlation coefficients are achieved by GBRT in predicting UCS (0.9887) and STS (0.9666), which respectively increase by 3.0%–10.8% and 16.0%–21.6% in comparison with the models in previous literature. The mechanical properties UCS and STS are highly sensitive to the curing age with relative importance of 36.8% and 40.3%, respectively. To facilitate the application of the tree-based models in predicting mechanical properties of manufactured-sand concrete, a graphical user interface has been designed in this study.