<p>The chemical industry plays a vital role in modern agriculture and economic development, yet it generates complex and variable wastewater streams that pose significant environmental and public health risks. Sustainable wastewater management is therefore, essential to ensure regulatory compliance, environmental protection, and long-term industrial viability. This study presents a structured segregation-based framework for chemical industry wastewater management, classifying streams into green, yellow, and red categories based on contamination levels and treatability to enable targeted treatment and resource recovery using biological processes, membrane technologies, advanced oxidation, and evaporation systems. Literature underscores the increasing importance of segregation, resource recovery and digitalization in enhancing treatment performance. Furthermore, we establish a data-driven techno-economic benchmarking framework using multi-year operational data from several industrial effluent treatment plants. This framework incorporates key performance indicators, including chemical oxygen demand removal efficiency, energy and chemical consumption, sludge generation, and maintenance costs, to generate composite efficacy indices. Our analysis reveals that power consumption, manpower, and maintenance represent the primary operational expenditures, identifying critical opportunities for process optimization. These findings demonstrate that sustainable wastewater management requires a strategic balance between treatment efficiency, operational economics, and resource recovery. By integrating artificial intelligence, machine learning, and digital twins, this framework provides a scalable, data-driven foundation for predictive and adaptive management in the chemical industry.</p>