Artificial Intelligence (AI) and Machine Learning (ML) technologies are transforming wastewater treatment and water quality management. This review explores the current trends and future prospects of integrating AI and ML in water treatment, including chlorination, adsorption, membrane filtration, and water quality monitoring. The integration of IoT, AI, and ML enables real-time monitoring, optimization, and automation of water treatment processes, improving efficiency and reducing costs. AI models such as Recurrent Neural Networks (RNNs), Artificial Neural Networks (ANNs), Fuzzy Neural Networks (FNNs), Deep Neural Networks (DNNs), and Convolutional Neural Networks (CNNs) are widely used for predicting water quality parameters, modeling water treatment processes, and monitoring effluent wastewater treatment in aquaculture systems. ML techniques, including Support Vector Machines (SVMs), Random Forests (RFs), and Artificial Neural Networks (ANNs), are effective in predicting and managing water quality, particularly in controlling disinfection by-products (DBPs) and optimizing wastewater treatment processes. These technologies also play a crucial role in surface water quality prediction, groundwater analysis, and drinking water management. Soft sensors, which use ML algorithms to estimate process parameters, are increasingly used in wastewater treatment facilities to replace expensive and unreliable sensors. Additionally, AI and ML are being applied to improve water infrastructure resilience, ensuring the system's ability to withstand natural disasters and climate change impacts. The review highlights the potential of AI and ML in addressing water-related challenges, emphasizing their role in sustainable water management and environmental protection.Artificial Intelligence (AI) and Machine Learning (ML) technologies are transforming wastewater treatment and water quality management. This review explores the current trends and future prospects of integrating AI and ML in water treatment, including chlorination, adsorption, membrane filtration, and water quality monitoring. The integration of IoT, AI, and ML enables real-time monitoring, optimization, and automation of water treatment processes, improving efficiency and reducing costs. AI models such as Recurrent Neural Networks (RNNs), Artificial Neural Networks (ANNs), Fuzzy Neural Networks (FNNs), Deep Neural Networks (DNNs), and Convolutional Neural Networks (CNNs) are widely used for predicting water quality parameters, modeling water treatment processes, and monitoring effluent wastewater treatment in aquaculture systems. ML techniques, including Support Vector Machines (SVMs), Random Forests (RFs), and Artificial Neural Networks (ANNs), are effective in predicting and managing water quality, particularly in controlling disinfection by-products (DBPs) and optimizing wastewater treatment processes. These technologies also play a crucial role in surface water quality prediction, groundwater analysis, and drinking water management. Soft sensors, which use ML algorithms to estimate process parameters, are increasingly used in wastewater treatment facilities to replace expensive and unreliable sensors. Additionally, AI and ML are being applied to improve water infrastructure resilience, ensuring the system's ability to withstand natural disasters and climate change impacts. The review highlights the potential of AI and ML in addressing water-related challenges, emphasizing their role in sustainable water management and environmental protection.