This study investigates a potential application of the adaptive neuro-fuzzy inference system (ANFIS) as a relatively new approach for predicting solar still productivity (SSP). Five variables, relative humidity (RH), solar radiation (SR), feed flow rate (MF), and total dissolved solids of feed (TDSF) and brine (TDSB), were used as input parameters. The data were collected from an experimental solar still system used to desalinate seawater in an arid climate. The data were distributed randomly into training, testing, and validation datasets. A hybrid learning algorithm and eight different membership functions were applied to generate the ANFIS models. Several statistical criteria were used to assess the model performances. The ANFIS model with a generalized bell membership function provided the best prediction accuracy compared with models with other membership functions. The coefficient of correlation values for this model were 0.999, 0.959, and 0.832 for training, testing, and validation datasets, respectively. Sensitivity analysis (SA) was used to show the effectiveness of the considered input parameters for predicting SSP. The SA results indicated that SSP is the most influential parameter on SSP. Generally, the findings indicate the robustness of the ANFIS approach for estimating SSP.