Rehabilitation is a crucial aspect of recovery for individuals affected by accidents, injuries, or medical conditions. Its objective is to restore functionality and enhance quality of life through a range of therapeutic techniques. This review emphasizes the pivotal role of electroencephalography (EEG) in advancing rehabilitation technologies, particularly through its integration with robotic systems. EEG devices, in conjunction with brain-computer interfaces (BCIs), offer profound insights into patient neural activities, enabling the tailored application of therapeutic exercises. Furthermore, machine learning techniques are employed to interpret EEG data, enhancing the precision and adaptability of rehabilitation interventions. This paper discusses the development and application of advanced machine learning algorithms that classify EEG signals for effective control of rehabilitation robots. These innovations promise to personalize treatment procedures, optimize recovery outcomes, and improve patient autonomy by facilitating direct brain-to-device communication. The continuous evolution of EEG and BCI technologies is set to revolutionize rehabilitation practices, offering new pathways to restore independence and improve the quality of life for patients globally.