Alpine headwater streams play a crucial role in the global carbon cycle and are particularly sensitive to climate change. Riverine organic matter (ROM) mediates the transport and transformation of terrestrial carbon across aquatic systems. However, the response of ROM in headwater streams on the Qinghai–Tibetan Plateau (QTP) to climate change remains poorly understood due to scarce in situ measurements. In this study, we used machine learning models combining satellite data and geographical variables to reconstruct historical variations in Chemical Oxygen Demand (COD, a proxy for ROM) along the Yellow River’s Headstream (YRHS), and to predict future changes under typical Shared Socioeconomic Pathways. The results indicate that COD levels in the midstream region of the YRHS, characterized by greater precipitation, higher soil organic matter, and denser vegetation, were relatively high (2.73 ± 1.63 mg L⁻¹) and exhibited an increasing trend (+0.44 mg L⁻¹) over the past decade. Driven primarily by increasing precipitation and temperature, COD levels are projected to rise in upstream and downstream areas but decline at midstream sites under SSP126, SSP245, and SSP585 by the 2100s. The annual export of COD from the midstream of the YRHS is expected to increase from 62.5 Gg to 81.6 Gg by 2100s due to projected increase in COD concentrations and discharge. Our findings identify the midstream region of the YRHS as a critical and climate-sensitive region for organic matter dynamics. Nevertheless, substantial uncertainties remain in the future ROM changes owing to the complex interactions among precipitation, warming, and their combined effects on carbon cycles in alpine catchments. Therefore, further research is required to improve our understanding of catchment-scale carbon dynamics on the QTP in the context of climate change.