Rapid urbanization in the plain river network of Changshu City has led to prominent water quality degradation and eutrophication risks. Traditional in-situ monitoring is constrained by sparse sampling and high costs, while conventional remote sensing approaches struggle with accurate water body extraction and stable parameter inversion in turbid, fragmented rivers. This study establishes a targeted remote sensing monitoring framework using 2024 multi-source data (Gaofen-2, Sentinel-2) and field measurements. An optimized modified Normalized Difference Water Index (mNDWI) combined with a spatially weighted adaptive threshold algorithm is adopted to precisely extract complex river networks. Based on Pearson correlation analysis, sensitive spectral bands and band combinations are screened for four key indicators: Chlorophyll-a (CHL-a), Total Nitrogen (TN), Total Phosphorus (TP), and Secchi Depth (SD). Statistical regression and weighted Principal Component Analysis-Random Forest (PCA-RF) models are developed for quantitative inversion, and their accuracy is verified using cross-validation with R² and RMSE. A weighted modified Carlson Trophic State Index suitable for plain river networks is applied for eutrophication assessment, and the single-factor pollution index method combined with the worst-factor principle is adopted to conduct comprehensive water quality evaluation in accordance with the national surface water environmental quality standards. The integrated inversion–evaluation–mapping workflow realizes spatially continuous water quality analysis, providing a reliable and region-adapted technical solution for remote sensing monitoring and scientific management of water environment in plain river network areas.