The article "Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective" by Ahmed E. Alprol, Abdallah Tageldein Mansour, Marwa Ezz El-Din Ibrahim, and Mohamed Ashour explores the integration of Internet of Things (IoT) and advanced AI techniques, such as machine learning (ML), in wastewater treatment and water quality prediction. The authors highlight the potential of these technologies to address global challenges related to clean water and sustainable systems. They discuss the transformative applications of smart IoT, AI, and ML models in various aspects of water treatment, including monitoring, simulation, and automation. Key applications include chlorination, adsorption, membrane filtration, and monitoring water quality indices. The article also reviews the performance and applications of artificial neural networks (ANN) and ML in different water bodies, such as surface water, groundwater, drinking water, and wastewater. Additionally, it covers the development of soft sensors for water treatment plants and the potential future applications of IoT and AI in water infrastructure resilience improvement. The review emphasizes the importance of data-driven approaches and the role of AI and ML in enhancing the efficiency and sustainability of water treatment processes.The article "Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective" by Ahmed E. Alprol, Abdallah Tageldein Mansour, Marwa Ezz El-Din Ibrahim, and Mohamed Ashour explores the integration of Internet of Things (IoT) and advanced AI techniques, such as machine learning (ML), in wastewater treatment and water quality prediction. The authors highlight the potential of these technologies to address global challenges related to clean water and sustainable systems. They discuss the transformative applications of smart IoT, AI, and ML models in various aspects of water treatment, including monitoring, simulation, and automation. Key applications include chlorination, adsorption, membrane filtration, and monitoring water quality indices. The article also reviews the performance and applications of artificial neural networks (ANN) and ML in different water bodies, such as surface water, groundwater, drinking water, and wastewater. Additionally, it covers the development of soft sensors for water treatment plants and the potential future applications of IoT and AI in water infrastructure resilience improvement. The review emphasizes the importance of data-driven approaches and the role of AI and ML in enhancing the efficiency and sustainability of water treatment processes.