Modern wastewater treatment plants face growing operational challenges due to increasingly variable influent compositions, stricter environmental regulations, and rising energy efficiency demands. This study provides a comprehensive evaluation of three advanced control strategies for optimizing wastewater treatment processes: conventional Proportional-Integral-Derivative (PID) control, fuzzy logic control, and the innovative Adaptive Neuro-Fuzzy Inference System (ANFIS). The research specifically focuses on addressing the critical need for intelligent systems capable of managing complex, non-linear relationships in key water quality parameters, particularly Total Dissolved Solids (TDSs) and water hardness concentrations. Through detailed MATLAB/Simulink simulations, we implemented each control methodology in a sophisticated wastewater treatment plant model that accurately replicates real-world operational conditions. The controller’s performance was rigorously assessed using multiple quantitative metrics: settling time, percentage overshoot, steady-state error, and energy consumption efficiency. Experimental results demonstrated that while the conventional PID controller achieved basic regulation, it exhibited significant limitations including 10% overshoot, prolonged 25-second settling time, and noticeable steady-state error. The fuzzy logic approach showed marked improvement, reducing overshoot to less than 1% and settling time to 13 seconds. The ANFIS controller outperformed both alternatives, delivering exceptional control precision with near-zero overshoot (0.2%), rapid 10-second response time, and complete elimination of steady-state error. Furthermore, the ANFIS system demonstrated superior adaptability to process variations while reducing energy consumption by 50% compared to traditional methods. These findings provide compelling empirical evidence that ANFIS-based control systems represent a transformative solution for next generation wastewater treatment infrastructure, offering unmatched performance in terms of both treatment quality and operational efficiency.