The increasing concern about environmental issues has encouraged experts to focus their attention on the proper operation and control of wastewater treatment plants. In the present study, the artificial neural network (ANN) method was used to model the effluent quality of the wastewater treatment plant in Bushehr. This method was chosen due to the ability of neural networks to simulate complex and nonlinear relationships between data, as well as its high accuracy in predicting water quality parameters. The neural network is able to show good performance even in situations where the input data has complex and unpredictable changes. Also, due to the power of learning from data and adapting to different conditions, ANN is a suitable tool for simulating and predicting wastewater quality parameters such as EC, TDS, COD, TSS, and BOD. The steps of this research include collecting input data from the wastewater treatment plant in Bushehr, which includes EC, TDS, COD, TSS, and BOD parameters. Then, the input data was applied to the artificial neural network for training and testing the model. These data include the measured values of wastewater at different stages of wastewater treatment. The neural network model was trained for each of the wastewater quality parameters and then its performance was tested and evaluated in different periods. The modeling results showed that the artificial neural network performed well in predicting and modeling these parameters. The outputs from the artificial neural network showed that this algorithm performed well in modeling quality parameters. In particular, in the first and second stages of treatment, high accuracy of the model in predicting wastewater quality was observed. Modeling the EC, TDS, COD, TSS and BOD parameters using artificial neural networks had different results at different stages of wastewater treatment. The results of modeling the different parameters showed that the accuracy of the model for the EC parameter reached i