Abstract. Water scarcity and environmental pollution remain among the most pressing global challenges of the 21st century, particularly in industrial regions. This study proposes an artificial neural network (ANN)-based predictive model for the intelligent control of water hardness (H) and total dissolved solids (TDS) mass concentration in the industrial wastewater treatment process using ion-exchange resins. Experimental data obtained from a pilot-scale purification system treating wastewater from the Kungrad Soda Plant in Uzbekistan were used to train and validate the model. The ANN was developed in MATLAB using a feedforward backpropagation algorithm, with H (in milligrams of calcium carbonate per litre, mg/L) and TDS (in mg/L) as input quantities and the servo valve opening degree (SerK) as the output quantity. The predictive model was trained on 80 experimental datasets and achieved high accuracy, with a mean squared error (MSE) of 9.72 × 10⁻⁴ and a regression coefficient R = 0.987, indicating a strong correlation between predicted and measured values. The trained ANN accurately modelled the nonlinear interdependence between influent water quality parameters and process control actions. For example, at input values of H = 2.0 mg/L and TDS = 20.0 mg/L, the model predicted a valve opening degree of 12.5%, which closely matched the empirical value. Similarly, when H = 3.43 mg/L and TDS = 35.5 mg/L, the model correctly predicted a minimised valve opening of 4.16%, confirming its predictive reliability across a broad operational range. These results demonstrate that the proposed ANN-based model can serve as an effective and reliable tool for real-time control and optimisation of wastewater treatment processes. Its ability to generalise from experimental data makes it particularly well-suited for dynamic and uncertain industrial environments, supporting smarter, data-driven decision-making in water resource management.