Abstract: The Yangtze-to-Huaihe Water Diversion Project is a major cross-basin water transfer initiative in China. The Henan section of the project plays a critical role in supplying water for domestic and industrial use in both urban and rural areas of eastern Henan Province, serving as a key component of the national water network. The water conveyance route in this section follows a long-distance open-channel design. Along the route, villages are densely distributed, and the channel runs adjacent to national highways. Moreover, the Qingshui River and Luxin Canal are unenclosed watercourses. This unique geographical environment significantly increases the risk of sudden water pollution incidents, which, if they occur, could pose a serious threat to water supply security. The optimal selection of water quality monitoring sections is a preparatory step for establishing an early warning system for sudden water pollution events. It not only enables real-time monitoring of river water quality and timely issuance of alerts upon detecting contamination, but also provides essential data for classifying sudden water pollution incidents. Therefore, conducting a scientifically sound and rational selection of water quality monitoring sections is crucial for promptly assessing water quality conditions and effectively responding to sudden pollution events. However, existing research still has certain limitations in addressing the optimal selection of water quality monitoring sections in the unique context of the Yangtze-to-Huaihe Water Diversion Project (Henan section). On one hand, most current studies have not fully considered the risks of sudden pollution resulting from frequent traffic activities along the long-distance open-channel water conveyance route. On the other hand, in the absence of historical monitoring data, quantitative analysis of evaluation indicators tends to be insufficiently precise, making it difficult to comprehensively reflect regional water quality conditions. Based on the actual characteristics of the Yangtze-to-Huaihe Water Diversion Project (Henan section) and after identifying the project’s risk sources, this study selects 11 evaluation indicators from four aspects—risk level, faulting, river characteristics, and operability—to construct an evaluation system. A combined weighting method based on game theory, integrating the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM), is employed for the optimal selection of water quality monitoring sections. This approach aims to address the shortcomings of existing research and provide robust support for water quality monitoring and the development of an early warning system for sudden water pollution in the project. The optimal selection of water quality monitoring sections for the Yangtze-to-Huaihe Water Diversion Project (Henan section) constitutes a fundamental task in establishing an early warning system for sudden water pollution incidents. It enables the timely acquisition of water quality data during pollution events, facilitating subsequent response measures. This paper employs a comprehensive evaluation method for selecting optimal water quality monitoring sections in the project and draws the following conclusions. The main risk sources identified for the two water conveyance rivers in the Henan section are associated with traffic accidents on highway bridges. With reference to relevant monitoring section layout standards, 12 high-risk points were selected as preliminary monitoring section locations. A game theory-based combined weighting method using AHP-EWM was established to develop a comprehensive evaluation system. By optimizing subjective and objective weights and incorporating fuzzy mathematics evaluation theory, six locations—Guozhuyuan, Hetang, Dingqiaokou, Shibanqiao, Zhaoxicun, and Houchenzha—were selected from the 12 preliminary sites as the water quality monitoring sections for the Yangtze-to-Huaihe Water Diversion Project (Henan section). These sections cover the main risk areas of the Qingshui River and Luxin Canal and satisfy the project’s requirements for real-time water quality monitoring. This outcome provides solid data support for establishing an efficient and reliable early warning system for sudden water pollution, demonstrating significant theoretical and practical value.