Inland waters consist of multiple concentrations of constituents, and solving the interference problem of chlorophyll-a and colored dissolved organic matter (CDOM) can help to accurately invert total suspended matter concentration ($C_{\mathit{tsm}}$). In this study, according to the characteristics of the Multispectral Imager for Inshore (MII) equipped with the first Sustainable Development Goals Science Satellite (SDGSAT-1), an iterative inversion model was established based on the iterative analysis of multiple linear regression to estimate $C_{\mathit{tsm}}$. The Hydrolight radiative transfer model was used to simulate the radiative transfer process of Lake Taihu, and it analyzed the effect of three component concentrations on remote sensing reflectance. The characteristic band combinations $B_{6/3}$ and $B_{6/5}$ for multiple linear regression were determined using the correlation of the three component concentrations with different bands and band combinations. By combining the two multiple linear regression models, a complete closed iterative inversion model for solving $C_{\mathit{tsm}}$ was formed, which was successfully verified by using the modeling data (R² = 0.97, RMSE = 4.89 g/m³, MAPE = 11.48%) and the SDGSAT-1 MII image verification data (R² = 0.87, RMSE = 3.92 g/m³, MAPE = 8.13%). And it was compared with iterative inversion models constructed based on other combinations of feature bands and other published models. Remote sensing monitoring $C_{\mathit{tsm}}$ was carried out using SDGSAT-1 MII images of Lake Taihu in 2022–2023. This study can serve as a technical reference for the SDGSAT-1 satellite in terms of remote sensing monitoring of $C_{\mathit{tsm}}$, as well as monitoring and improving the water environment.