<p>Water pollution caused by dyes, heavy metals, and organic pollutants poses a significant global challenge, necessitating the development of effective and sustainable treatment technologies. Although traditional methods such as adsorption and ultrafiltration are widely used, their optimization is often hindered by complex and nonlinear process dynamics. This systematic and critical review, conducted in accordance with PRISMA guidelines, evaluates the integration of machine learning (ML) techniques in adsorption-based wastewater treatment to improve prediction accuracy and operational efficiency. A model-specific assessment of key approaches, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), Decision Trees (DT), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), is presented, highlighting their strengths and limitations. A key contribution of this work is the introduction of a unified data-to-model pipeline tailored for adsorption systems, encompassing data preparation, feature selection, and performance validation. While many studies report exceptionally high predictive accuracies (<i>R</i>² &gt; 0.99), this review provides a cautionary perspective on overfitting risks and limited generalization to real-world industrial effluents. Furthermore, emerging research directions are discussed, including Explainable Artificial Intelligence (XAI), hybrid physics-informed models, and Internet of Things (IoT)-enabled monitoring. Overall, this review offers a structured perspective for developing scalable, AI-driven wastewater treatment solutions.</p> Graphical Abstract <p></p>