Excessive erosion and fine sediment delivery to river corridors and receiving waters degrade aquatic habitat, add to nutrient loading, and impact infrastructure. Understanding the sources and movement of sediment within watersheds is critical for assessing ecosystem health and developing management plans to protect natural and human systems. As our changing climate continues to cause shifts in hydrological regimes (e.g., increased precipitation and streamflow in the northeast U.S.), the development of tools to better understand sediment dynamics takes on even greater importance. In this research, advanced geomatics and machine learning are applied to improve the (1) monitoring of streambank erosion, (2) understanding of event sediment dynamics, and (3) prediction of sediment loading using meteorological data as inputs. Streambank movement is an integral part of geomorphic changes along river corridors and also a significant source of fine sediment to receiving waters. Advances in unmanned aircraft systems (UAS) and photogrammetry provide opportunities for rapid and economical quantification of streambank erosion and deposition at variable scales. We assess the performance of UAS-based photogrammetry to capture streambank topography and quantify bank movement. UAS data were compared to terrestrial laser scanner (TLS) and GPS surveying from Vermont streambank sites that featured a variety of bank conditions and vegetation. Cross-sectional analysis of UAS and TLS data revealed that the UAS reliably captured the bank surface and was able to quantify the net change in bank area where movement occurred. Although it was necessary to consider overhanging bank profiles and vegetation, UAS-based photogrammetry showed significant promise for capturing bank topography and movement at fine resolutions in a flexible and efficient manner. This study also used a new machine-learning tool to improve the analysis of sediment dynamics using three years of high-resolution suspended sediment data collected in the Mad River watershed. A restricted Boltzmann machine (RBM), a type of artificial neural network (ANN), was used to classify individual storm events based on the visual hysteresis patterns present in the suspended sediment-discharge data. The work expanded the classification scheme typically used for hysteresis analysis. The results provided insights into the connectivity and sources of sediment within the Mad River watershed and its tributaries. A recurrent counterpropagation network (rCPN) was also developed to predict suspended sediment discharge at ungauged locations using only local meteorological data as inputs. The rCPN captured the nonlinear relationships between meteorological data and suspended sediment discharge, and outperformed the traditional sediment rating curve approach. The combination of machine-learning tools for analyzing storm-event dynamics and estimating loading at ungauged locations in a river network provides a robust method for estimating sediment production from catchments that informs watershed management.