The integration of robotics and artificial intelligence has opened new horizons in personalized rehabilitation therapy. This research focuses on the development of intelligent exoskeleton systems that leverage machine learning algorithms to provide adaptive and patient-specific support during physical rehabilitation. Traditional rehabilitation approaches often rely on standardized protocols that may not accommodate individual patient needs, leading to suboptimal recovery outcomes. The proposed system uses real-time sensor data, including kinematic and physiological signals, to monitor patient movement and dynamically adjust the exoskeleton’s assistance levels. Machine learning models analyze the patient’s progress and predict optimal movement patterns, enabling personalized therapy plans that evolve over time. Experimental evaluations demonstrate that the intelligent exoskeleton improves mobility, reduces rehabilitation duration, and enhances patient engagement compared to conventional methods. This study highlights the potential of combining AI and wearable robotics to revolutionize rehabilitation practices, offering scalable, adaptive, and data-driven therapeutic solutions.