The study was aimed at developing a simple methodology for flow prediction in ungauged basins using existing data resources. For this purpose, the streamflow measurements across the Kentucky River Basin located in Kentucky, USA were obtained from United States Geological Survey (USGS) archive. The flow transferring characteristics of the subbasins of the Kentucky River Basin were obtained by combining downstream and upstream stream gauges. The flow transferring function thus derived were related to watershed, channel and flow characteristics of the subbasins by multiple regression analysis. The gauge pairs were divided into two classes of subbasins representing Upper and Lower Kentucky, which were characterized mainly by the geology of the watersheds. The regression models corresponding to the two groups of subbasins were applied to example gauge pairs to evaluate the efficiency of the proposed model to predict streamflow in downstream channel. The estimated hydrographs agreed with the observed hydrographs with the performance efficiency of greater than 90%. The proposed method was tested for its applicability in first-order streams in the Goose Creek, a tributary to the Kentucky River. The overland flow component for the first-order streams was determined using TOPMODEL with topography, soil and climatic factors as inputs. The overland flow was routed to the Goose Creek outlet using the transfer function obtained from measured flow records. The simulated hydrographs were reproduced with 80% accuracy when compared with the observed hydrographs. The flow prediction of first-order ungauged streams was automated by the back-calibration algorithm. The algorithm is supported by the Shuffled Complex Evolution - University of Arizona algorithm for its optimization routine. The back-calibration procedure optimizes each first-order stream with the aid of the flow transferring function. The back-calibration procedure was imbedded in a Visual Basic.NET environment to automatically predict flow on a daily time scale and predicted was published on the internet using ESRI Arc Internet Mapping Server (ArcIMS). The project thus provides daily streamflow estimation for streams on a first-order level on every day basis, which will facilitate flow prediction of streams regardless of the size of the watersheds.