In this work Random Forest (RF) and Response surface methodology (RSM) were used to model and predict the efficiency of malachite green removal from aqueous solution by ultrasound-assisted adsorption onto the silver hydroxide nanoparticles loaded on activated carbon (AgOH-NPs-AC). The prepared nanoparticles were characterized by SEM, FTIR, XRD and TEM. The parameters such as pH, initial MG concentration, sonication time and adsorbent dosage involved in the adsorption process were set within the ranges 2.0-10, 4-20 mg L −1 , 2-6 min and 0.005-0.025 g, respectively. The performance of the RF and CCD models for the description of experimental data was evaluated in terms of the coefficient of determination (R 2 ), the root mean squared error (RMSE), mean absolute error (MAE) and absolute average deviation (AAD). The obtained results showed that the RF model outperformed in comparison to classical statistical model for modeling the process of dye adsorption. Desirability function approach (DFA) and Genetic algorithm (GA) combined central composite design (CCD) as global optimization technique were used for simultaneous optimization of effective factors. GA and DFA were revealed that 20 mg L −1 MG by 0.025 g AgOH-NPs-AC at pH = 8.0 and sonication time for 6 min with adsorption capacity 40.98 and 41.99 mg g −1 can be removed, respectively. The equilibrium adsorption data were analyzed by Langmuir Freundlich, Temkin and Dubinin-Radushkevich isotherm models. The best fit to the data was obtained from the Langmuir model.Meanwhile, the maximum adsorption capacity for MG by 0.01, 0.02 and 0.025 g of AgOH-NPs-AC was estimated 57.143, 42.735 and 40.980 mg g −1 , respectively.Analysis of experimental adsorption data to various kinetic models shows the applicability of the second-order equation model. The variable importance principle also shows that RF give maximum importance to sonication time for removal of MG.