The pulp and paper industry is a major contributor to industrial wastewater discharge, necessitating advanced treatment strategies to ensure environmental compliance. This study investigates the application of computational intelligence (CI) models for predicting wastewater treatment performance using real-world operational data. Five CI models—decision trees (DT), random forests (RF), genetic programming (GP), artificial neural networks (ANN), and support vector machines (SVM)—were evaluated for accuracy and reliability. Among them, SVM demonstrated superior predictive performance, effectively capturing complex nonlinear relationships in wastewater treatment processes. The results highlight the potential of CI models to transform industrial data into actionable insights, optimizing treatment efficiency and enabling sustainable water reuse. This research provides a systematic framework for integrating CI techniques into pulp and paper mill wastewater treatment, paving the way for data-driven decision-making and enhanced regulatory compliance.
