Abstract: Existing prediction models for vehicle speed in open-pit mines do not fully consider the influence of meteorological factors on operating speed. The data sample sets are insufficient to train or fine-tune deep learning models. Production planning and dynamic adjustment of the number of dispatched equipment according to weather conditions have not been implemented, resulting in substantial redundancy of equipment in the transportation process and failing to achieve the goal of refined management in open-pit mines. To address this issue, this study proposed a vehicle speed characteristic analysis method and a speed prediction model based on spatiotemporal and meteorological factors. Clustering analysis and correlation analysis were used to obtain the speed characteristics of vehicles under different meteorological and spatiotemporal conditions. By integrating factors such as road location, operating period, and meteorological conditions, a prediction model for vehicle speed in open-pit mines was derived for scenarios including straight roads, uphill sections, turns, and downhill sections under no-precipitation, light-rain, moderate-rain, heavy-rain, and snowfall conditions. A model for safe vehicle speed was constructed based on parameters such as road friction coefficient, driver reaction time, and vehicle braking distance, yielding the safe operating speeds for vehicles in different spatiotemporal scenarios. The analysis results showed that: ① the average speed of empty vehicles was higher than that of loaded vehicles; the difference in average speed between empty and loaded vehicles was significant in turns and downhill sections, while it was relatively small in straight and uphill sections. ② Vehicle speed was negatively correlated with precipitation and strongly correlated with road structure and temporal distribution. ③ The speed prediction model achieved a mean absolute percentage error ≤ 3%, a mean absolute error ≤ 0.4 km/h, and a root mean square error <3 km/h, demonstrating good predictive performance.