A system (1000) for predicting freezing of gait (FOG) in users is presented. The system (1000) includes pressure sensors (101) embedded in the insoles of footwear (100), capturing pressure values during walking. A first processor (1001) within the insoles generates a pressure profile and extracts time-stamped gait parameters (103). Additionally, flexible electronic sensors (201) embedded in a pair of knee joint flexion units (200) detect joint flexion parameters. An electronic sensor processing unit (300) integrates inputs from both, the first processor (1001) and flexible sensors (201), computing postural indices based on predefined rules. The strategy generator module (306) then compares these gait parameters (103) and postural indices with pre-stored user data, applying an AI-based model to predict FOG. The system�s multi-sensor approach provides real-time analysis of gait and posture, facilitating early detection of FOG, which is crucial for improving mobility and quality of life in individuals susceptible to such episodes.