Abstract: For the insufficient prediction accuracy and systematic bias in traditional single machine learning model for elective surgery scheduling, this study innovatively proposes a multi-model fusion framework based on the grey possibility function, which improves prediction accuracy through a grey possibility-function-based representative fusion mechanism. A grey representative prediction system was constructed by integrating machine learning algorithms such as gradient boosting, random forest, and decision trees. Notably, this system introduces an innovative three-layer surgical duration classification framework that autonomously adjusts prediction parameters based on the characteristics of surgical durations. This feature effectively addresses the traditional model’s tendency to underestimate long surgeries and overestimate shorter ones. In the verification phase, multidimensional evaluation is conducted using prediction accuracy indicators and scheduling efficiency indicators. Experimental data shows that the new framework achieves a 0.13% reduction in MAE indicators and a 3.0% increase in R². In terms of surgical scheduling efficiency, the average total overlap time was optimized to 8.81 minutes, a decrease of 36.48%. More importantly, the prediction bias of long and short surgeries narrowed to -14.2% and +45.4%. This research provides reliable data support for the allocation of operating room resources. By accurately predicting the surgery duration, it can improve the utilization rate of operating rooms, optimize the scheduling of medical staff, and rationalize the scheduling of medical equipment, ultimately promoting the overall improvement of medical service quality.