This chapter delves into the transformative role artificial intelligence (AI) plays in neurocognitive rehabilitation. The theoretical foundations, diversity in applications, benefits, challenges, and future potential of AI is explored in depth. Integrating AI in rehabilitation practices leverages high-end technology to enhance cognitive recovery, motor function, and overall quality of life for individuals with neurological disorders and other psychiatric conditions. The overview of AI technology and its capacity to integrate core principles of neuroplasticity has vital functions in neuropsychological rehabilitation. Interventions that apply AI focuses on the immersive quality, ecological validity, and real-time feedback mechanisms along with benefits of repetitive, task-oriented practice. Applications specifically explored in this context are applied in stroke rehabilitation for motor and cognitive rehabilitation and in traumatic brain injury (TBI) to aid executive functions. In case of Parkinson’s disease, tailored AI tasks that address gait and motor deficits have displayed promising outcomes in comparison to traditional rehabilitation approaches. Autism spectrum disorder (ASD) utilizes tools that enhance social skills and sensory integration. Post-traumatic stress disorder (PTSD) benefits from AI’s capacity to aid exposure therapy. The present chapter discusses the role of personalization and adaptability in rehabilitation settings encouraging interventions and in meeting tailored needs of each patient. The gamification of tasks fosters engagement and motivation and provides safe and controlled environments that simulate real-world scenarios. One of the key features of AI is the ability to design interventions that are specific to patients’ needs, reducing the load of selecting appropriate interventional models and tasks on the therapists. Additional home-based and tele-rehabilitation strategies increase the availability of rehabilitation and high-quality interventions for the patients, further reducing caregiver burden. Challenges in rehabilitation include the persisting lack of ethical regulation and data mining without consent, limitations of longitudinal evidence for increased efficacy have significant limitations. Future research can shift focus to its various limitations to ensure high-yielding research outcomes are obtained.