This article introduces a novel framework integrating generative artificial intelligence (AI) with robotic process automation (RPA) to dynamically manage hospital operations through real-time predictive scheduling. The proposed system continuously monitors patient inflow patterns, clinical urgency indicators, and resource availability to autonomously adjust surgery schedules, intensive care unit bed allocations, and staff rotations. The implementation across multiple hospital departments demonstrates significant improvements in emergency department throughput, reduction in surgical cancellations, and optimized staff utilization while maintaining quality of care standards. The system's machine learning components demonstrated increasing accuracy in predicting resource demands over the implementation period, enabling proactive rather than reactive scheduling adjustments. This article addresses critical inefficiencies in traditional hospital resource allocation methodologies by leveraging AI-enabled automation to respond to changing conditions in real time. The article suggests that intelligent scheduling systems can substantially improve hospital operational efficiency while enhancing both patient and provider experiences in high-pressure clinical environments.