Temperature is one of the most important environmental influences on aquatic organisms. It is a primary driver of physiological rates and many abiotic processes. However, despite extensive research and measurements, synoptic estimates of water temperature are not available for most regions, limiting our ability to make systemwide and large-scale assessments of aquatic resources or estimates of aquatic species abundance and biodiversity. We used subwatershed averaging of point temperature measurements and associated multiscale landscape habitat conditions from over 3,300 lotic sites throughout New York State to develop and train artificial neural network models. Separate models predicting water temperature (in cold, cool, and warm temperature classes) within small catchment–stream order groups were developed for four modeling units, which together encompassed the entire state. Water temperature predictions were then made for each stream segment in the state. All models explained more than 90% of data variation. Elevation, riparian forest cover, landscape slope, and growing degree-days were among the most important model predictors of water temperature classes. Geological influences varied among regions. Predicted temperature distributions within stream networks displayed patterns of generally increasing temperature downstream but were patchy due to the averaging of water temperatures within stream size-classes of small drainages. Models predicted coldwater streams to be most numerous and warmwater streams to be generally associated with the largest rivers and relatively flat agricultural areas and urban areas. Model predictions provide a complete, georeferenced map of summer daytime mean stream temperature potential throughout New York State that can be used for planning and assessment at spatial scales from the stream segment class to the entire state.