Wastewater management poses a global challenge. Integrating data-driven models has significantly enhanced treatment facilities' design and operational efficiency. In this study, an Artificial Neural Network (ANN) algorithm was adapted as a time-series forecasting model to predict effluent TN (Total Nitrogen) and TP (Total Phosphorus) concentrations in a real municipal wastewater treatment plant (WWTP). For this purpose, six independent TN and TP models were developed and evaluated using Mean Absolute Percentage Error (MAPE, %) and Root Mean Square Error (RMSE) metrics. Based on these criteria, all models demonstrated similar performance, with MAPE and RMSE values for TN forecasting at approximately 12% and 1.4, respectively, in the test phase. The MAPE was approximately 30% for TP forecasting, and RMSE was 0.25. Upon completing the modeling studies, one model was integrated into a user-friendly graphical user interface (GUI) and tested with actual data, allowing users to obtain results with a single click.