Machine Learning in Water Treatment is a must-have for anyone interested in how artificial intelligence is transforming water treatment, offering practical insights, case studies, and a deep dive into cutting-edge machine learning techniques that can improve water quality management. Machine Learning in Water Treatment explores the complex fields of wastewater treatment and water purification, offering a thorough analysis of the cutting-edge machine learning methods used to solve problems with water quality control. It provides insights into how artificial intelligence can be incorporated with conventional procedures, bridging the gap between conventional water treatment techniques and state-of-the-art data-driven solutions. The book will cover the foundations of water treatment procedures, providing insights into the ideas behind physical, chemical, and biological treatment modalities. Difficulties in managing water and wastewater quality are paving the way for the use of machine learning as an effective tool for control and optimization. Fundamentally, the book explains how machine learning models are used in water treatment system control, optimization, and predictive modeling. Readers will learn how to take advantage of machine learning algorithms’ potential for real-time treatment process optimization, quality issue identification, and water pollutant level prediction through a thorough investigation of data collection, preprocessing, and model creation. Case studies and real-world applications provide insightful information about the application of machine learning technologies in a variety of scenarios. With its unique combination of theoretical understanding and real-world applications, this book is an invaluable tool for understanding how water quality management is changing in the age of data-driven decision-making.