Monitoring phytoplankton classes in river networks is critical to understanding phytoplankton dynamics and to predicting the ecosystem response to changing land-use and seasons. Applicability of phytoplankton fluorescence as a quick and effective ecological monitoring approach is relatively unexplored in freshwater ecosystems. We used multivariate analyses of fluorescence from pigment extracted in 90% acetone to assess the variability in phytoplankton classes, herbivory, and organic matter quality in a freshwater river network. A total of four models developed by the parallel factor analysis (PARAFAC) of fluorescence excitation and emission matrices identified six components: Model 1 (pheophytin-A and chlorophyll-A), Model 2 (chlorophyll-B and chlorophyll-C), Model 3 (pheophytin-B), and Model 4 (pheophytin-C). Redundancy analyses revealed that in the summer, urban and agricultural streams were abundant in chlorophylls, fresh organic matter, and organic nitrogen, whereas in winter, streams were high in phaeopigments. A slow-moving, light-limited wetland stream was an exception as high phaeopigment abundance was observed in both seasons. The PARAFAC components were used to develop a partial least-squares regression-based model (r2 = 0.53; Nash-Sutcliffe efficiency = 0.5; n = 147) that successfully predicted chlorophyll-A concentrations from an external subset of river water samples (r2 = 0.41; p < 0.0001; n = 75). Thus, combining multivariate analyses and fluorescence spectroscopy is useful for monitoring and predicting phytoplankton dynamics in large river networks.