Membrane wetting is a bottleneck that limits the widespread application of membrane distillation (MD) technologies. However, the prediction of membrane wetting is difficult, due to its unpredictable behavior with the chemical species in feed waters. We used response surface methodology (RSM) and artificial neural networks (ANN) to predict the wetting phenomena in direct contact membrane distillation (DCMD) for the treatment of synthetic wastewater. Experiments were performed at various concentrations of NaCl, CaSO4, humic acid, alginate, and sodium dodecyl sulfate (SDS) to examine their effects on the wetting. The RSM and ANN models were established using the experimental data and statistically validated by the analysis of variance (ANOVA). The results showed that both RSM and ANN are able to predict the time of wetting and recovery for the range of input variables. The model predictions suggested that the concentration of NaCl and SDS has the greatest influence on the prediction parameters. When the concentration of SDS was less than 5mg/L, the concentration of NaCl was the dominant role in the wetting. On the other hand, the concentration of SDS was the predominant factor when the concentration of SDS was higher than 5mg/L.