The intelligent control of industrial wastewater treatment processes, especially for the removal of residual chlorine ions, is crucial for ensuring environmental safety, protecting infrastructure from corrosion, and optimizing operational efficiency. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control model was developed and implemented for real-time control of a laboratory-scale activated carbon filtration unit. The primary objective was to dynamically regulate the chlorine removal process by adjusting the influent flow rate and activated carbon dosage based on predicted output parameters. The ANFIS model was trained using a dataset of 200 experimental trials that encompassed six key process variables: flow rate (m³/h), initial chlorine concentration (mg/L), system pressure (bar), pH, temperature (°C), and activated carbon dose (kg). These six parameters served as input variables, while the output control targets included residual chlorine concentration (mg/L), energy consumption (kWh), and overall system efficiency. The designed ANFIS controller actively modulated the flow rate and carbon dosage in response to changes in initial chlorine concentration, temperature, and pH, thereby ensuring optimal chlorine removal under varying conditions. Compared to a traditional Proportional–Integral–Derivative (PID) controller, the ANFIS-based system demonstrated superior performance in dynamic tracking, disturbance rejection, and steady-state regulation. Specifically, ANFIS achieved a 72.4% reduction in steady-state error, decreased settling time by 2.7 seconds, and enhanced overall prediction accuracy, as confirmed by regression values exceeding 0.96. Additionally, optimized control actions by ANFIS led to a 1.6% reduction in energy consumption compared to PID, contributing to more sustainable process operation. These results confirm the efficacy of ANFIS in handling nonlinear, multivariable industrial processes and highlight its potential for integration into full-scale intelligent supervisory control systems such as SCADA. The research demonstrates that the hybrid use of neural networks and fuzzy inference provides a flexible, data-driven solution for real-time control in industrial water treatment applications.