Abstract: Long-term hydrological forecasting is an important technology for the efficient utilization of water resources. In this study, we synthesize global research advances in long-term hydrological forecasting and examine operational needs specific to the Changjiang River Basin, proposing an intelligent framework for next-generation forecasting systems. We first systematically review three methodological categories, that is traditional statistical approaches, machine learning techniques, and coupled hydrological-meteorological modeling, and analyze their key limitations. Second, we describe the current status of long-term hydrological forecasting operations in the Changjiang River Basin, as well as the challenges faced in terms of accuracy, uncertainty, and other aspects. Building on this, we propose an innovative framework for an intelligent, science-based, and efficient long-term hydro-meteorological forecasting system. This system integrates five key modules: climate pattern diagnostics ("Encyclopedia"), precipitation trajectory modeling ("Precipitation Navigator"), adaptive hydrological analysis ("Smart Basin Analyst"), predictive benchmarking ("Evaluation and Recommendation"), and dynamic product generation ("Output Engine"). It aims to improve the accuracy and timeliness of long-term hydrological forecasting and provide robust technical support for the scientific management of water resources in the Changjiang River Basin.