Water quality index is the most convenient way of communicating water quality status of water bodies, but its evaluation requires subjectivity in terms of user involvement and dealing with uncertainty. Recently, artificial intelligence algorithms that are appropriate for nonlinear forecasting and also dealing with uncertainties have been applied to various domains of water quality forecasting. This paper focuses on development of a data-driven adaptive neurofuzzy system for the water quality index using a real data set obtained from eight different monitoring stations across River Satluj in northern India. Novelty in the paper lies in the estimation of water quality index using two different clustering techniques: fuzzy C-means and subtractive clustering-based ANFIS and assessing their predictive accuracy. Each model was used to train, validate, and test the index that was obtained from seven water quality parameters including pH, conductivity, chlorides, nitrates, ammonia, and fecal coliforms. The models were evaluated on the basis of statistical performance criteria. Based on the evaluations, it was found that the SC-ANFIS method gave more accurate result as compared to the FCM-ANFIS. The tested model, SC-ANFIS model, was further used to identify those sensitive parameters across various monitoring stations that were capable of causing change in the existing water quality index value.