Response surface methodology (RSM) is a statistical and mathematical approach used for optimizing processes, particularly in livestock wastewater (LWW) treatment. This review summarizes the main steps of RSM, recent applications in LWW treatment, and highlights its advantages and limitations. RSM is used to optimize operational parameters that affect process performance, leading to improved treatment efficiency, reduced environmental impact, and cost savings. The process involves experimental design, data collection, response surface modeling, model evaluation, model interpretation, optimization, and validation. RSM has been successfully applied in various LWW treatment processes, including biological, physical, and chemical treatments, to optimize parameters such as temperature, pH, hydraulic retention time, and substrate concentration. However, RSM has limitations, such as simplified modeling assumptions, variability in real-world conditions, and the need for accurate experimental data. Future research directions include integrating advanced modeling techniques, dynamic RSM, multi-objective optimization, sensitivity analysis, techno-economic analysis, and real-time monitoring and control. Additionally, considering environmental, social, and stakeholder aspects in RSM optimization is important for sustainable and socially acceptable LWW treatment. Overall, RSM provides a systematic approach for optimizing LWW treatment processes, leading to more efficient and sustainable wastewater management.Response surface methodology (RSM) is a statistical and mathematical approach used for optimizing processes, particularly in livestock wastewater (LWW) treatment. This review summarizes the main steps of RSM, recent applications in LWW treatment, and highlights its advantages and limitations. RSM is used to optimize operational parameters that affect process performance, leading to improved treatment efficiency, reduced environmental impact, and cost savings. The process involves experimental design, data collection, response surface modeling, model evaluation, model interpretation, optimization, and validation. RSM has been successfully applied in various LWW treatment processes, including biological, physical, and chemical treatments, to optimize parameters such as temperature, pH, hydraulic retention time, and substrate concentration. However, RSM has limitations, such as simplified modeling assumptions, variability in real-world conditions, and the need for accurate experimental data. Future research directions include integrating advanced modeling techniques, dynamic RSM, multi-objective optimization, sensitivity analysis, techno-economic analysis, and real-time monitoring and control. Additionally, considering environmental, social, and stakeholder aspects in RSM optimization is important for sustainable and socially acceptable LWW treatment. Overall, RSM provides a systematic approach for optimizing LWW treatment processes, leading to more efficient and sustainable wastewater management.