Frozen gait (FG) is an increasingly prevalent concern in individuals with Parkinson’s disease (PD) that limits mobility and increases the risk of falls. Traditional FG detection and monitoring methods using clinical observations and wearable sensors face limitations, such as inflexibility, lack of portability, inaccessibility to individuals, and the inability to provide continuous monitoring in real-life environments. To address these challenges, this experimental study presents the development of a software-defined radio (SDR)-based radio frequency (RF) sensing platform for continuous FG monitoring. Data were collected through multiple experiments involving various physical activities, including FG episodes. The acquired data were processed using advanced signal-processing (ASP) techniques to extract relevant wireless channel state information (WCSI) patterns. The physical activities were classified using machine learning and deep learning models developed on the dataset prepared from the SDR-based RF sensing system. The results demonstrated that the deep learning models outperformed the machine learning models. The bidirectional gated recurrent unit (BiGRU) achieved the highest accuracy of 99.7%. This indicates that the developed system has the potential for accurate, real-time monitoring of FG and other PD symptoms. The proposed RF sensing platform using SDR technology and artificial intelligence (AI) offers an intelligent and continuous monitoring solution, addressing the limitations of traditional methods. This system provides portable, continuous detection of FG events, potentially improving patient care, safety, and early intervention.