This research explores the utilization of artificial neural networks (ANN) and response surface methodology (RSM)/central composite design (CCD) to create a system that can remove MB from synthetic wastewater by means of a chitosan derivative. The process of cross-linking and grafting formulated chitosan adsorbent material. On the other hand, RSM-CCD was utilized for the experiment design in the adsorption investigations. The input parameters were adsorbent dosage, pH, contact time, and MB concentration, while a single neuron in the output layer represented the removal efficiency of MB. The RSM and ANN models were assessed utilizing statistical metrics such as average relative errors (ARE), coefficient of determination (R2), mean squared error (MSE), Pearson’s Chi-square (χ2), root mean square errors (RMSE), and sum of squares of errors (SSE). In various conditions (pH 7, concentration 125 mg/L, adsorbent mass 8.0 g, contact duration 60 min), the optimal removal percentage for MB adsorption, as indicated by RSM results, was 98.7%. The isothermal studies indicated that a maximum adsorption capacity of 130.57 mg/g aligned well with the Langmuir model. R2 values of 0.99964, 1, and 1 during the training, validation, and testing phases exemplify the optimal performance of the trained neural network. The results indicated that the ANN outperformed the RSM-CCD model method. The findings indicate that artificial neural networks (ANN) can effectively forecast the removal of MB from wastewater. The capacity to regenerate the beads was assessed during five successive adsorption/desorption trials.