Wastewater treatment is essential for minimizing pollutants in discharges and preserving the quality of water resources. The selection of an effective method and technology for wastewater treatment has become a major challenge due to the diversity, types, and quantities of pollutants. The wastewater treatment process is intricate and influenced by numerous environmental factors and process parameters, which subsequently impact the treatment efficiency and the quality of the effluent. Recently, artificial intelligence (AI) as well as Response Surface Methodology (RSM) have played a significant role in addressing the challenges associated with wastewater treatment. AI has been extensively applied in the operation and management of wastewater treatment plants (WWTPs), monitoring and predicting water quality, as well as optimizing processes. It refers to using machine learning and advanced algorithms to improve operational efficiency, real-time monitoring, and predictive maintenance, leading to better decision-making, reduced costs, and improved environmental outcomes. On the other hand, RSM is a statistical technique used to optimize processes by evaluating the relationships between multiple variables. It involves designing experiments to understand how different factors affect a particular response variable. The integration of Artificial Intelligence (AI) and Response Surface Methodology (RSM) has emerged as a powerful approach for optimizing wastewater treatment processes. This combination enhances predictive capabilities and operational efficiency, allowing for improved management of various wastewater types. The current chapter aims to provide an overview of the current developments, challenges, objectives and trends in AI and RSM research related to wastewater treatment, based on published articles and patents.