Postoperative pain control presents an essential healthcare obstacle because conventional methods depend on established medication schedules and pain perception evaluation methods. This paper describes the NSAI-QATS as a real-time intelligent framework which uses neuro-symbolic reasoning along with QI-RL and federated learning to manage therapy personalization through integration with modern sensor-based tools. The system depends on wearable biosensors together with AR/VR interaction tracking to collect real-time physiological information which comprises of heart rate variability (HRV), electroencephalogram (EEG) signals, and galvanic skin response (GSR). NSAI-QATS employs biofeedback-based adaptive therapy selection to adjust pain relief approaches because it uses individual patient responses for decision-making. Experimental outcomes established that NSAI-QATS provides a reduced Mean Squared Error of 0.22 along with a moderate level of positive correlation (r = 0.62) between heart rate variability measurements and pain reduction levels. The results of statistical assessment demonstrate the superior pain relief effectiveness of AI-based adaptive VR therapy over static therapy protocols (p = 0.0004). This research demonstrates how the system performs patient classification into three therapy response clusters (high, moderate, low responders) for implementing customized pain treatment methods. Future research activities will concentrate on two main areas including advanced scalable implementation of federated learning and reinforcement learning model optimization alongside multi-week therapy assessment development.