Machine Learning (ML) technologies play a role in facilitating the delivery of remotely therapeutic interventions. Over recent years, a range of methods have been suggested to help in remote monitoring and intelligent support within rehabilitation services. Both supervised and unsupervised ML algorithms have been recognized as non-invasive motion capture technologies, identified as strategic applications that facilitate remote and intelligent monitoring. Thus, we aim to find a technological solution for rehabilitation of children with cerebral palsy (CP) by providing different types of therapy and facilitating ways for rehabilitation-based-home. We propose an intelligent Framework that aims to help patients by offering advanced learning methods for personalized rehabilitation tailored to each patient. In this context, we suggest an E-health Educational Environment, which uses artificial intelligence technologies to ensure that learning is adapted to the patient's needs, progress and learning styles and to improve the quality by making it more individualized and efficient.