A precise estimation of natural gas water content is a significant constraint in appropriate planning of natural gas production, processing services and transmission. The main contribution of this research is to develop a machine learning approach for predicting water content of sweet and sour natural gases. In this regard, a joining of particle swarm optimization and an artificial neural network was utilized. The suggested model presents good predictions of the sour natural gas water content with following circumstances, including CO2 contents of 0–40 mol%, H2S contents of 0–50 mol%, pressures in range from atmospheric to 70,000 KPa for sour gas and 100,000 KPa for sweet gas, and temperatures from 10–200°C for sweet gases and 10–150°C for sour gases.
