At various parametric conditions such as Jet Pressure (JP), Cutting Distance (CD), and Cutting Speed (CS) with distinct levels using Reduced Error Pruning Tree (REPTree) in Decision Tree (DT) classification, an attempt was made to estimate the abrasive water-jet system process properties on AlSi7/SiC composite. In DT learning, the REPTree classification algorithm is used as a representative strategy. Machining input parameters such as JP, SOD, CS of the DT are independent of the simple algorithmic properties of the process. Subtree structures that can be replaced by a leaf node with class mark and attribute values that are independent of each other with the help of DT under consideration. The idea of a uniform tree analysis that leads to the pruning of the number of subtrees is extended to the tree. The proposed strategies minimize computing costs, improve the speed and precision of the performance of the device.