A Brain-Computer Interface (BCI) is a network device that converts brain activity into a desired mechanical action. A modern BCI action would entail the utilization of a brain-activity analyzer and neural networking algorithm to collect, interpret, and translate complicated brain signals for a machine. A robotic arm, a voice box, or any automated assistive equipment, such as prosthetics, wheelchairs, and iris-controlled screen cursors, could be among these machines. Artificial intelligence (AI) and machine learning (ML) in particular enable a better knowledge of brain activity as well as improved brain-computer interface (BCI) connection mechanisms. Early diagnosis and accurate non-pharmacological treatment of neurological diseases and disorders can be achieved by incorporating AI/ML into the data collecting and monitoring phases of neuromodulation or neurofeedback. Furthermore, machine learning allows for the analysis of huge amounts of patient data in order to improve the efficacy of neuromodulation and neurofeedback.