Artificial Intelligence has been increasingly into cognitive, motor and Natural language processing training applications in the neurorehabilitation area, proposing adaptive and personalized solutions for neurological disorders. This research will explore the design of an AI-Driven Dynamic Content Generation system aimed at providing personalized cognitive training tasks, together with runtime adaptation, in accordance with the user’s performance and emotional state. It dynamically adjusts the difficulty of the task to be optimally challenging yet achievable, using advanced machine learning techniques in concert with face recognition. Additionally, there is a module of a medical assistant that runs separately, using OpenAI with the integration of a medical knowledge base, so that users can find reliable information about medical things. It further personalizes the system by suggesting educational videos that are related to the queries asked by the users. The emotion detection model was subject to extensive experimentation using the network Xception in a method of transfer learning; quite efficient, accurate, and reliable results were found concerning classification related to recognizing emotions. The medical assistant was rigorously tested for responsiveness and accuracy, ensuring concise and informative answers. Together, these components form a cohesive system that not only supports cognitive improvement but also promotes user engagement and motivation through adaptive gamification and interactive learning. This research highlights the potential of combining AI-driven personalization and real-time adaptability to create impactful solutions for neurorehabilitation and cognitive enhancement