This research aims to describe the hybrid algorithm's effectiveness in predicting and optimizing the abrasive water jet machining (AWJM) parameter on flexible sheep wool reinforced polyester composites. Selected five parameters are transverse speed (TS), water jet pressure (WJP), nozzle stand-off distance (NSoD), reinforcement weight percentage (wt%) and abrasive size (AS). In contrast, Surface Roughness (R_a) and Kerf Angle (K_a) are output performances. Multi objective optimization by ratio analysis (MOORA) is a tool is used for selecting and optimizing control variables. The most influential control variables are AS, WJP, TS, wt%, and NSoD, according to MOORA–Entropy feature selection results. The support vector machine algorithm (SVM) represents the AWJM process, and the model's performance is compared to SVM hybrid models. The differential evolutionary (DE) algorithm and the Entropy idea create a hybrid model. An SVM model is compared with the Hybrid SVM—Entropy model; hybrid improves prediction performance by 21.6%. When the MOORA—SVM—Entropy hybrid model is compared to the SVM model, it is revealed that the MOORA—SVM—Entropy hybrid model's prediction performance improves by 38.7%. According to the MOORA—Entropy approach, the optimal control variables are A₂, B₁, C₁, D₃, and E₁.