This book presents a comprehensive and practical overview of machine learning-driven adsorption processes for pollution removal from wastewater, with a focus on modeling, optimization, and mechanistic insights. It explores how techniques such as Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Response Surface Methodology (RSM) can enhance the efficiency of removing heavy metals—including Chromium (VI), Copper (II), Cadmium (II), and Zinc (II)—using biodegradable and nanostructured adsorbents like modified cellulose nanocrystals. Through detailed case studies, experimental methodologies, and comparative analysis of AI algorithms, this book bridges traditional adsorption science with advanced computational approaches, offering valuable tools and insights for researchers, engineers, and practitioners working in environmental science, chemical engineering, and sustainable water treatment.