Influent water quality conditions are the key elements required to investigate and optimize the management of sewage treatment plants, and timely acquisition of influent water quality data is of vital importance. In view of the fact that five-day biochemical oxygen demand (BOD₅), a key water quality indicator of sewage plants, is difficult to directly detect and has strong hysteresis, four methods including the back-propagation artificial neural network (BP-ANN), grid search algorithm (GS) optimized support vector regression (SVR), particle swarm optimization (PSO) improved SVR, and genetic algorithm (GA) improved SVR were adopted to establish soft sensing models of influent BOD₅ by using the mathematical relationship between BOD₅ and other influent parameters to achieve the rapid determination of influent BOD₅. A sewage plant in Heilongjiang province was taken as the research object, and the performance of the four machine learning models was compared to find a soft sensing method suitable for the prediction of influent BOD₅. Results show that the prediction results of the soft sensing model based on SVR were better than that based on BP-ANN, and the GA optimized SVR model had the highest accuracy, which provides reference for the real-time monitoring of BOD₅ and convenient management of sewage treatment plants.