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Response surface methodology (RSM), Artificial Neural Network (ANN) and Radial Basis Function Neural Network (RBFNN) were applied to model and predict the efficiency of two carcinogenic dyes (Methylene blue (MB) and Malachite green (MG)) adsorption onto Mn@ CuS/ZnS nanocomposite-loaded activated carbon (Mn@ CuS/ZnS-NC-AC) as a novel adsorbent. The properties of Mn@ CuS/ZnS-NC-AC were identified by XRD; FE-SEM and EDS. The parameters such as pH, Mn@ CuS/ZnS-NC-AC mass, sonication time, MB concentration and MG concentration involved in the adsorption process were set within the ranges 4.0–8.0, 0.010–0.030 g, 1–5 min, 5–25 mg L⁻¹ and 5–25 mg L⁻¹, respectively. The applicability of the RBFNN, ANN and RSM models for the description of experimental data was examined using four statistical criteria (coefficient of determination (R²), root mean square error (RMSE), mean …