Measurement of the nutrient concentrations in the stream is usually done on weekly, biweekly or monthly basis due to limited resources. There is need to estimate concentration and loads during the period when no data is available. The objectives of this study were to test the performance of a suite of regression models in predicting continuous water quality loading data and to determine systematic biases in the prediction. This study used the LOADEST model which includes several predefined regression models that specify the model form and complexity. Water quality data primarily nitrogen and phosphorus from five monitoring stations in the Neuse River Basin in North Carolina, USA were used in the development and analyses of rating curves. We found that LOADEST performed generally well in predicting loads and observation trends with general tendency/bias towards overestimation. Estimated Total Nitrogen (TN) varied from observation (“true” load) by -1% to 9%, but for the Total Phosphorus (TP) it ranged from -2% to 27%. Statistical evaluation using R², Nash-Sutcliff Efficiency (NSE) and Partial Load Factor (PLF) showed a strong correlation in prediction.