Wrist and hand injuries are commonly seen in novice Kyokushin Karate practitioners, but injuries can contribute to functional impairments and pain. Such injuries often prolong rehabilitation periods, affecting both performance and the ability to continue training. Generalized treatment protocols with a limited approach is common in traditional rehabilitation approaches which are unable to adapt to real-time feedback on the recovery of the athlete. The goal of this study was the investigation of the feasibility of an artificial-intelligence based rehabilitation program enriched with motion tracking and an electrical muscle stimulation (EMS) therapy application with the potential to improve the recovery outcome parameters, while additionally providing grip strength and pain reduction in athletes undergoing the recovery phase from moderate wrist injuries. The study uses a pre-test and post-test experimental design to assess improvements in grip strength, pain level, and range of motion (ROM) in an attempt to quantify the restorative influence of AI-rehabilitative support. The results show a marked improvement in participants undergoing AI based therapy versus traditional rehabilitation. Grip strength increased from 36.9 kg to 48.27 kg, and VAS (Visual Analog Scale) pain also decreased from 3.25 to 0.25. Wrist flexion also improved in flexion from 40° to 65°, showing increased joint mobility and functional recovery. The findings of this approach suggest that an AI-driven biomechanical platform can adapt rehabilitation protocol with real-time feedback to accurately reflect the biomechanical response of the athlete and provide personalized trajectories towards improved recovery. AI-powered rehabilitation harnesses real-time monitoring, predictive analytics, and adaptive therapy adjustments to outperform the efficacy, efficiency, and data-driven basis of conventional methods. Future studies could develop larger datasets through monitoring rehabilitation programs using AI coupled with wearable devices and using deep learning-based motion tracking models for optimization of injury management. Furthermore, the incorporation of AI-powered rehabilitation into multiple sports categories might unlock tremendous potential in hyper-personalized approaches of automated injury prevention, accelerated recovery insights, and better performance diagnostics.