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The present work aimed to compare the predictive power of response surface methodology (RSM), general regression neural network (GRNN), and artificial neural network (ANN) in modelling of an azo dye (AzD) uptake from aqueous solutions by a new developed nanocomposite (NC) based on the graphene sheets and hydroxyapatite/ZnO nanoparticles. The successful fabrication was confirmed by the techniques of Fourier transform infrared (FTIR), Raman, X-ray diffraction (XRD), Brunauer, Emmett and Teller (BET), scanning electron microscope (SEM), Energy-dispersive X-ray spectroscopy (EDX) and mapping. The optimum adsorption conditions based on Genetic algorithm (GA) and desirability function (DF) offered the maximum removal of 94.83% and 96.5%, respectively. RSM, GRNN and ANN, showed the accurate and robustness performance in forecasting AzD adsorption performance. The statistical error …