Freezing of Gait (FoG) is one of the most disabling motor symptoms of Parkinson’s disease (PD), often leading to falls and reduced quality of life. This paper proposes AiCarePWP, a deep learning-based framework designed to forecast FoG episodes before they occur, enabling preventive interventions. Leveraging wearable sensor data, AiCarePWP employs temporal modeling to capture subtle gait dynamics and detect precursors of FoG events. Experimental evaluation shows that AiCarePWP achieves high predictive accuracy and robustness compared to conventional detection approaches, offering a path toward real-time, patient-centric monitoring systems for Parkinson’s disease management.