Understanding the drivers of stream trophic states is crucial for evaluating freshwater ecosystem health and guiding effective management strategies. This study examines which indicators are most effective for assessing spatial variation in stream trophic states by comparing a range of biochemical and physical properties with landscape features. Using monthly water samples collected from streams ranging from headwater tributaries to river outlets in a temperate lowland region of the Great Lakes Basin, we developed an analytical framework that integrates Principal Component Analysis (PCA) to assess stream ecosystem services. The PCA analysis based on water properties explained approximately 85% of the variance in annual mean trophic states, which increased to 91% with the inclusion of landscape features. Using principal components as variables, the linear regression model based on landscape features explained 63% of the variance in suspended chlorophyll-a concentrations (R² = 0.63), outperforming traditional models based on water properties (R² = 0.55). Despite ongoing challenges in quantifying benthic chlorophyll biomass (BCHL), our linear models based on either water properties or landscape features showed comparable correlations with field measurements (R = 0.57 and R = 0.56, respectively). These findings highlight the strong predictive power of landscape features for assessing stream trophic states, offering a scalable approach for regional water quality assessments.