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A multiple linear regression (MLR) model and least square support vector regression (LS-SVM) model with principal component analysis (PCA) was used for preprocessing to predict the efficiency of methylene blue adsorption onto copper oxide nanoparticle loaded on activated carbon (CuO-NP-AC) based on experimental data set achieved in batch study. The PCA-LSSVM model indicated higher predictive capability than linear method with coefficient of determination (R²) of 0.97 and 0.92 for the training and testing data set, respectively. Firstly, the novel nanoparticles including copper oxide as low cost, non-toxic, safe and reusable adsorbent was synthesized in our laboratory with a simple and routine procedure. Subsequently, this new material properties such as surface functional group, homogeneity and pore size distribution was identified by FT-IR, SEM and BET analysis. The methylene blue (MB) removal and …