<p>Nowadays, water scarcity is one of the most pressing global issues in the <br /> world. The application of resource-efficient technologies in the treatment of industri-<br /> al wastewater is of significant importance. This study focuses on the intelligent con-<br /> trol of the industrial wastewater treatment process. Initially, we developed and tested <br /> a wastewater treatment device using ion-exchange resins on a mixture of industrial <br /> wastewater from the Kungrad soda plant. Based on the obtained results, an automat-<br /> ic control system was designed to regulate water hardness (H) and total dissolved <br /> solids (TDS) based on the wastewater flow rate. The Adaptive Neuro-Fuzzy Inference <br /> System (ANFIS) was implemented as the control system. The results demonstrated <br /> that the ANFIS-based control system reduces adjustment time by 25–30% and de-<br /> creases static error by 12% compared to conventional PID and fuzzy logic control <br /> methods. This led to a reduction of up to 1.3% in the energy consumption of the servo <br /> valve that regulates water flow. Additionally, to enable real-time monitoring and au-<br /> tomatic control of the process, software was developed based on the MasterSCADA <br /> 4D platform. This system provides continuous monitoring and optimization of water <br /> quality parameters (H and TDS).</p>