This article addresses the persistent inefficiencies in hospital human resource management (HRM), specifically in workforce planning, staff scheduling, and performance evaluation, which traditional manual approaches have failed to resolve. The authors propose an integrated AI-driven HRM framework comprising three coupled modules: (1) workforce demand forecasting using LSTM, XGBoost, and Random Forest models; (2) intelligent staff scheduling via constrained optimization incorporating legal, contractual, skill-based, and preference-aware parameters; and (3) performance evaluation combining structured metrics with NLP-analyzed unstructured feedback. Experiments conducted on both synthetic and real hospital datasets report LSTM achieving the highest forecasting accuracy (MAE = 6.1, R² = 0.91), the scheduling module reducing conflicts by 41% with a Gini fairness coefficient of 0.08, and NLP analysis revealing 74% positive patient feedback. Pilot deployments yielded an 18% reduction in patient waiting times and a 14% improvement in satisfaction scores, with computational scalability confirmed for up to 1,000 staff members. The authors position the framework's novelty in its end-to-end integration and decision-linked pipeline rather than in any single component, situating it within the broader digital health transformation agenda. Key insights: LSTM outperformed XGBoost and Random Forest in workforce demand forecasting, achieving MAE of 6.1 and R² of 0.91 on hospital admission prediction tasks. The AI-powered scheduling module reduced scheduling conflicts by 41% and achieved a Gini coefficient of 0.08, indicating high equity in shift distribution, while remaining computationally scalable to 1,000 staff members with solver times under 95 seconds. Integrating NLP-based unstructured feedback (patient surveys, peer reviews) with structured performance metrics produced performance profiles that identified 74% positive sentiment and actionable departmental insights, addressing the subjectivity limitations of traditional supervisor-based appraisals. Practical takeaways: The three-module pipeline architecture — forecasting feeding directly into scheduling constraints, which in turn informs performance evaluation — demonstrates a technically viable approach to coupling traditionally siloed HRM functions in hospital settings. Pilot deployment results, including 18% reduction in waiting times and 14% improvement in satisfaction scores, suggest measurable operational impact, though these figures derive from limited single-site pilots and require independent multi-site replication before broader conclusions can be drawn.