Reliable assessment of freshwater ecosystem condition requires a biological framework that remains interpretable across space and time without dependence on regional calibration. This study develops a universal and ecoregion-independent relative bioassessment using National Rivers and Streams Assessment (NRSA) data. In this approach, eight widely macroinvertebrate-metrics were applied, each providing complementary structural and functional ecological signals. A percentile-based Index of Biotic Integrity (IBI) was constructed to support the relative bioassessment, avoiding predefined regional indices and absolute ecological thresholds. All sites were classified as good (top 25%), average (middle 75%) and poor (bottom 25%). The study employed year-specific Generalized Linear Models, Random Forests and eXtreme Gradient Boosting (XGboost) models that consistently identified non-linear relationships between key physical and water-chemistry drivers and IBI. Furthermore, Shapley Additive Explanations (SHAP)-based attribution from XGBoost models identified a stable set of influential stressors that persisted across years despite regional heterogeneity. To quantify uncertainty and sensitivity by Monte Carlo simulation, an XGBoost model was trained exclusively with physical and water-chemistry drivers to predict IBI. The prediction performed moderately due to the intentional exclusion of responsive biotic metrics from the model. Meanwhile, scenario-based simulations revealed a clear leftward shift of IBI distribution under realistic degraded conditions compared with baseline variability. Importantly, this framework enables the identification of regionally agnostic driver ranges beyond which biological condition exhibits measurable decline, independent of predefined ecological thresholds. Thus, the model framework provides a transferable and policy-relevant approach for interpreting biological change under real-world stressors without depending on absolute thresholds.