Flow regimes, characterized by magnitude and seasonality dynamics, exert critical controls on ecological communities across spatial scales, with growing alterations from climate change and anthropogenic interventions. Effective ecological restoration requires advancing mechanistic understanding of flow-ecology relationships across time. This study presents a hybrid attribution framework integrating seasonality analysis and machine learning to investigate flow-ecology coupling in China’s Han River Basin. Through systematic analysis of characteristic flow with climate variables, we identify precipitation, temperature and potential evapotranspiration (PET) as dominant climatic controllers of extreme flow events. For flow-ecology relationship establishment, we develop an induced machine learning architecture combining structural equation modeling, correlation analysis with LSTM-Transformer networks, achieving high predictive accuracy (R2 = 0.8) for riparian normalized difference vegetation index (NDVI) dynamics. The framework’s prognostic capability is demonstrated through 2025–2035 projections under the SSP2-4.5/5–8.5 scenarios, revealing temperature and PET as pivotal causal drivers of riparian NDVI variability. To efficiently obtain the flow sequences under the future climate scenarios, the study constructs two optimization algorithm-based LSTM-Transformer coupled models, achieving superior simulation results with NSE exceeding 0.95 during the historical period (1981–2023). Future NDVI projections indicate that ecosystem productivity increased with phenological diversity under the SSP2-4.5 scenario, while NDVI dynamics under the SSP5-8.5 scenario reveals vegetation homogenization and increased heat stress. This work contributes to process-aware attribution methodology for ecohydrological systems, providing actionable insights for ecological flow management in climate-stressed basins. The hybrid framework demonstrates transferable potential for deciphering complex flow-ecology interactions across regulated river systems.