The efficient removal of phosphorus from wastewater is a critical step in wastewater treatment plants (WWTPs) to prevent eutrophication and ensure water quality standards. This study investigates the application of support vector machines (SVMs) for the chemical phosphorus removal process in WWTPs. The SVMs that can accurately estimate the phosphorus removal efficiency based on input variables such as influent phosphorus concentration, chemical dosages, and process operating conditions. The SVM algorithm is selected for its ability to handle highdimensional datasets and capture complex nonlinear relationships within the data. To achieve this goal, historical process data from an operational WWTP is collected and preprocessed to build the SVM model. The SVM model is trained using a portion of the dataset and validated using the remaining data to ensure its robustness and generalizability. Furthermore, sensitivity analysis is performed to identify the most influential input variables affecting phosphorus removal efficiency. The insights gained from the sensitivity analysis can aid in process optimization and decision-making in real-time applications. The enactment of the model is related with other commonly used regression techniques to evaluate its superiority in predicting phosphorus removal efficiency. The results demonstrate that SVMs exhibit excellent predictive capabilities for the chemical phosphorus removal process in WWTPs, achieving high accuracy and robustness. The sensitivity analysis identifies the influent phosphorus concentration and chemical dosages as the key factors affecting phosphorus removal efficiency.