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Parkinson's disease (PD) is the fastest-growing neurodegenerative disease affecting 90 thousand new Americans each year. PD includes motor and non-motor symptoms, resulting in progressive disability and difficulty in completing activities of daily living. Freezing of Gait (FoG) is one of the common disabling symptoms of PD, characterized by difficulties in initiating walking, resulting in gait abnormalities and increased risk of falling (RoF) and fear of falling (FoF). Clinical management of FoG is difficult as it is minimally responsive to both pharmacological and surgical interventions. In fact, these interventions can paradoxically worsen of FoG. Additionally, PD patients with FoG have reported worse health-related quality of life (HR-QoL) due to limitations in mobility, activities of daily living (ADL), bodily discomfort, stigma, and social isolation. Despite its increasing treatment and management of FoG is difficult due to its paroxysmal and heterogeneous nature. Therefore, there is a growing need for effective, evidence-based management and intervention approaches for FoG. Some current techniques used to manage FoG are physical therapy, exercise, gait training, and balance training; however, due to a lack of patient adherence, accessibility concerns, and the need for continuous supervision and individualized feedback, the long-term effectiveness of these interventions remains limited and challenging to achieve in real-world settings. A new promising avenue for managing PD is the use of wearable technology, which can provide audiovisual, via augmented and virtual reality (AR/VR), and tactical cueing to offset FoG, thereby enhancing independence in PD patients. In this comprehensive review, we will provide an overview of the symptoms, monitoring, and treatment of PD, with a focus on the neuroanatomy and treatment of FoG. We will review and critique the extant literature on the use of AR/VR technology in the management of FoG. Finally, the challenges and risks associated with wearable technology in FoG management will also be identified.