Abstract: To address the low efficiency in production information utilization and weak decision-making support problems caused by insufficient data value mining and inadequate system optimization in key production processes of mining-stripping operation chains in China’s open-pit coal mines, this study innovatively proposes a fusion methodology combining data analytics and system optimization for the mining-stripping operation chain of the open-pit coal mine. First, the fusion mode and the fusion modeling approach for data analytics and system optimization of the mining-stripping operation chain are proposed. Then, the data analytics and optimization fusion approach addressing critical issues in key production processes of the mining-stripping operation chain are elaborated, and the tests are conducted based on actual data from different open-pit coal mines to verify the effectiveness of the proposed fusion approach. The results show that the proposed fusion approach outperforms existing counterparts in terms of performance, the geological model dynamic reconstruction technology based on data analysis achieves sub-meter-level fitting accuracy in the mining and stripping areas and maintains the error rate of coal reserves below 3%; the data-driven prediction technology significantly enhances the forecast accuracy of the coal production, with a maximum prediction error rate of less than 2.5%; meanwhile, the dynamic optimization technology of the mining-stripping plan improves the synergy of production resources, leading to a 12% increase in production efficiency; the data analysis-based dynamic scheduling technology for mining/stripping equipment significantly improves the scheduling level of the truck fleet, resulting in a significant reduction in the average stripping haulage distance and a substantial improvement in equipment transportation efficiency. By integrating the data analytics and system optimization, this study effectively resolves real-world challenges such as insufficient safety monitoring, low-precision geological modeling, poor-quality production plans, as well as inefficient and unstable equipment operation.