To quantitatively reveal the research status, knowledge structure, and development trends of automated suspended sediment monitoring technologies and their applications, this study retrieved relevant literature from the Web of Science Core Collection (1976–15 April 2026). After screening, 1926 publications were analyzed using Bibliometrix, VOSviewer, and CiteSpace, covering collaboration networks, co-citation, historical direct citation, keyword co-occurrence clustering, and burst detection. The results indicate sustained growth in this field: China ranks first in publications, and the Chinese Academy of Sciences is the most productive institution. The research is markedly interdisciplinary, encompassing environmental science, earth science, oceanography, remote sensing, and water resources; Remote Sensing of Environment, Continental Shelf Research, and Journal of Geophysical Research: Oceans are important journals in the co-citation network. Co-citation analysis shows that the knowledge base comprises three pillars: ocean-color remote sensing retrieval and atmospheric correction, in-situ observation and hydrodynamic transport of suspended sediment, and sediment dynamics and environmental responses across watersheds, estuaries, and coastal zones. Historical direct-citation analysis further reveals that key findings have advanced along three trajectories: in-situ acoustic and optical monitoring, remote sensing retrieval in highly turbid waters, and riverine–wetland applications and large-scale sediment research. Over the past six years, hotspots have centered on multi-source remote sensing and water optical monitoring, machine learning and intelligent remote sensing data processing, sediment dynamics and in-situ monitoring, and river sediment transport and estuarine-coastal sediment processes. Keyword-burst analysis shows machine learning exhibiting the strongest recent burst since 2023, while research on sediment load and flux based on automated suspended sediment monitoring technologies has become active since 2024, indicating the field’s expansion from automated acquisition of suspended-sediment concentration toward multi-source fusion, intelligent retrieval, and sediment-transport discharge estimation.