In addressing the challenge of coordinating multiple machines during autonomous maize grain harvesting and transportation under a one-sided unloading configuration, a collaborative scheduling strategy was introduced based on a dynamic unloading-threshold-optimized Markov decision process (MDOP). By continuously adjusting the harvester’s unloading threshold in real time, the proposed approach enabled seamless interaction between harvesters and grain transport vehicles, thereby ensuring that the harvester maintained uninterrupted operational efficiency. This dynamic adjustment mechanism significantly reduced the harvester’s nonproductive waiting time and curtailed both the transportation cost incurred by the vehicles and the grain loss that occurred during transfer. To evaluate its performance, the MDOP-based collaborative strategy was benchmarked against two alternative models: the conventional full-bin-triggered unloading protocol and a genetic-algorithm-optimized unloading strategy. Under identical field conditions, the MDOP approach achieved an 18.1%, 4.9% reduction in total operational time compared with the conventional approach, while transportation costs were lowered by 8.9%, 19.3%. Moreover, the grain transfer loss rate under the MDOP regime was measured at approximately 4.3%, underscoring its ability to mitigate kernel spillage more effectively than competing methods. These results confirmed the superior efficacy and robustness of the MDOP-based scheduling strategy in multimachine cooperative tasks. By optimizing unloading thresholds dynamically, it not only preserved the continuous harvesting pace of the combines but also minimized idle intervals and logistical overhead. Consequently, this research laid a theoretical and practical foundation for realizing fully autonomous, multi-machine cooperative operations in maize harvesting, thereby furnishing critical technological support for the development of unmanned maize farming systems.