Adaptive deep brain stimulation (aDBS) is a promising therapy for Parkinson's disease (PD) that has the potential to improve treatment outcomes and reduce side effects compared to traditional, continuous DBS. In aDBS, the stimulation parameters are adjusted in real-time based on feedback from the patient's brain signals or symptoms. This allows for more precise and personalized stimulation, tailored to the patient's needs and fluctuating symptoms. Several studies have demonstrated the feasibility and effectiveness of aDBS in PD patients, with improvements in motor symptoms, quality of life, and reduction in medication usage. Furthermore, aDBS may have the potential to reduce the incidence of DBS-related side effects, such as speech and gait disturbances, as well as decrease battery usage and prolong device longevity. Despite the promising results, aDBS is still in the early stages of development and requires further research to fully understand its mechanisms and optimize its parameters. Challenges remain in identifying the optimal biomarkers for aDBS, designing closed-loop systems that can effectively integrate feedback signals and stimulation parameters, and assessing long-term efficacy and safety. In conclusion, aDBS is a promising approach that has the potential to revolutionize DBS therapy for PD. Further research is needed to refine the technology, identify the optimal biomarkers, and determine the most effective stimulation strategies for individual patients.