The detection of industrial discharge in wastewater systems is critical for maintaining environmental standards and public health. This chapter explores the utility of online instrumentation combined with supervised machine learning (ML) techniques for real-time monitoring and detection of industrial pollutants. The chapter begins by highlighting the limitations of traditional methods for detecting industrial discharge, such as delayed analysis and manual sampling. It introduces advanced online instrumentation, including sensors and analyzers, which provide continuous monitoring of wastewater parameters. These instruments measure key indicators such as pH, conductivity, and chemical composition, offering immediate insights into wastewater quality. Key areas of discussion include the integration of supervised ML algorithms with online instrumentation to enhance detection accuracy and response times. The chapter explains how ML models are trained using historical data to recognize patterns and anomalies indicative of industrial discharge. The role of data preprocessing, feature selection, and model validation in developing robust ML models is also explored. Case studies from various industries illustrate successful applications of these technologies, demonstrating improved detection capabilities and faster response to potential contamination events. Economic and environmental benefits, such as reduced compliance costs and minimized ecological impact, are highlighted. By providing a comprehensive overview, this chapter aims to inform industry stakeholders about the advantages of combining online instrumentation with supervised ML, promoting the adoption of advanced technologies for effective industrial discharge monitoring and environmental protection.