Effective allocation of nurse resources in surgical departments is essential for improving patient care and controlling operating costs in a health society. Length of stay (LOS) is the metric that connects clinical workload to staffing decisions, yet ward-level forecasting and its translation into daily nursing schedules remain limited. This study presents a hybrid, data-driven decision-support system that combines machine-learning LOS prediction with Reinforcement Learning (RL) for the surgical ward. A dataset of 137,145 records is used to evaluate Random Forest, Gradient Boosting, Decision Tree, and a Multi-layer Perceptron. Random Forest achieved the most accurate and stable performance (R² = 0.84; RMSE = 1.63), and its predicted LOS states drive an RL agent that adjusts staffing and triggers early-discharge reviews. The novelty lies in focusing on the understudied surgical ward, converting predicted LOS into a daily scheduling policy, and integrating forecasting with RL-based scheduling. The hybrid model reduced average LOS from 6.12 to 4.82 days, lowered weekly nurse overtime by approximately 47%, and improved staff utilization.