This research looked into whether machine learning algorithms into a hand exoskeleton rehabilitation system benefited persons with partial paralysis. Five people participated in the study by training with the exoskeleton for a period of one month. This was a mechanism for training ML models via electromyography (EMG), including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Decision Trees (DT), to make adaptive adjustments in exoskeleton parameter values. All participants saw their flexion and extension angles improve considerably as they went through this month-long regimen of exercises. In terms of accuracy, performance evaluation indicators precision, recall, and F1 score reflect the ML models’ success at predicting outcomes of rehabilitation. An ANN model with precision of 0.85, recall of 0.82, F1 score of 0.83 and an overall accuracy of 0.86 also demonstrated strong performance in guiding personalized interventions, particularly through feedback. Similarly, SVM and DT models performed well but with differing degrees of precision, recall, F1 score, and accuracy. On a level of individual accuracy, confusion matrices confirm the classification results of each ML mode. These results point the way for ML methods as a way of designing more effective rehabilitation strategies, greatly improving motor function for people with partial paralysis. With data from EMG sensors in real-time, this proposed exoskeleton-based rehabilitation provides a way to improve both range and functionality. Many future efforts in research are striving to continue refining the system and proving its efficacy in different clinical circumstances, in the end to improve the lives of those with paralysis.