Sustainable surface water quality management requires a thorough understanding of the influence of streamflow fluctuations on water quality across different seasons. The objective of this study is to explore the seasonal sensitivity of previously developed XGBoost-BO ensemble machine learning model to predict Water Quality Index (WQI) using streamflow as the sole input. This study applied Shapley Additive explanations (SHAP) and Partial Dependence Plots (PDP) to interpret the response of the developed models across four distinct seasons of Australia. The analysis reveals that WQI predictions exhibit varying degrees of sensitivity to streamflow in different seasons indicating critical thresholds during distinct flow events. The novelty of this study lies in its independent contribution of conducting seasonal sensitivity analysis using advanced explainability tools. This study investigates how and why the model responds differently to streamflow variations. The findings reveal season specific threshold, non-linear behaviors and contrasting flow-response patterns, providing non interpretability insights which are not captured earlier. The findings of the study highlight the significance of season specific interpretation of machine learning models in water quality management particularly under climate variability and provides insight into the perception of hydrological influences on water quality dynamics by the application of explainable artificial intelligence tools.