The water quality index (WQI) is an important tool for water resource management and planning. However, it has major disadvantages: the generation of chemical waste, is costly, and time-consuming. In order to overcome these drawbacks, we propose to simplify this index determination by replacing traditional analytical methods with ultraviolet-visible (UV-Vis) spectrophotometry associated with artificial neural network (ANN). A total of 100 water samples were collected from two rivers located in Assis, SP, Brazil and calculated the WQI by the conventional method. UV-Vis spectral analyses between 190 and 800 nm were also performed for each sample followed by principal component analysis (PCA) aiming to reduce the number of variables. The scores of the principal components were used as input to calibrate a three-layer feed-forward neural network. Output layer was defined by the WQI values. The modeling efforts showed that the optimal ANN architecture was 19-16-1, trainlm as training function, root-mean-square error (RMSE) 0.5813, determination coefficient between observed and predicted values (R) of 0.9857 (p < 0.0001), and mean absolute percentage error (MAPE) of 0.57% ± 0.51%. The implications of this work's results open up the possibility to use a portable UV-Vis spectrophotometer connected to a computer to predict the WQI in places where there is no required infrastructure to determine the WQI by the conventional method as well as to monitor water body's in real time.