The measurement and statistical modeling of water quality data are essential to developing a region-based stream-wise database that would be of great use to the EPA's needs. Such a database would also be useful in bio-assessment and in the modeling of processes that are related to riparian vegetation surrounding a water body such as a stream network. With the help of easily measurable data, it would be easier to come up with database-intensive numerical and computer models that explain the stream water quality distribution and biological integrity and predict stream water quality patterns. Statistical assessments of nutrients, stream water metallic and non-metallic pollutants, organic matter, and biological species data are needed to accurately describe the pollutant effects, to quantify health hazards, and in the modeling of water quality and its risk assessment. The study details the results of statistical nonlinear regression and artificial neural network models for Upper Green River watershed, Kentucky, USA. The neural network models predicted the stream water quality parameters with more accuracy than the nonlinear regression models in both training and testing phases. For example, neural network models of pH, Conductivity, Salinity, Total Dissolved Solids, and Dissolved Oxygen gave an R2 coefficient close to 1.0 in the testing phase, while the nonlinear regression models resulted in less than 0.6. For other parameters also, neural networks showed better generalization compared with nonlinear regression models.