Hospitals must allocate beds and staff effectively under volatile arrival patterns, where scheduling errors can cause preventable harm. This study introduces a fairness-aware, forecast-informed reinforcement learning framework for hospital scheduling, explicitly integrating fairness constraints, short-term demand forecasts, and SHAP-based explainability. The state fuses patient and system context with short-horizon demand forecasts, mean arrivals ($\widehat{\lambda }$), and volatility (${\widehat{\sigma }}^2$). The reward jointly optimizes efficiency, equity, and safety by penalizing waiting, diversions/transfers, ICU misuse, overtime, and cross-ward disparity. Using a benchmark-aligned synthetic cohort (60k visits over one year), the approach is compared against First-Come-First-Served (FCFS) and ablations without forecast features. The learned policy halves the mean waiting time (from 215.3 to 102.5 min), reduces diversions/transfers (from 27.6% to 7.8%), improves ICU match accuracy (from 93.4% to 95.1%), raises the fairness index by 45%, and cuts staff overtime by 56%. Adding forecast signals yields further gains over forecast-naive DQN (9% shorter waits; 28% fewer diversions/transfers), with robustness under demand surges and triage-mix shifts. By unifying equity constraints, anticipatory context, and explanation, the method turns reactive queues into proactive, auditable control and is extensible to perioperative flow, disaster triage, and outpatient capacity management.