Parkinson's Disease (PD) is a neurodegenerative disease which shows a deficiency in dopaminehormone in the brain. It is a common irreversible impairment among elderly people. Identifying this disease in its preliminary stage is im-portant to improve the efficacy of the treatment process. Disordered gait is one of the key indications of early symptoms of PD. Therefore, the present paper introduces a novel approach to identify parkinsonian gait using raw vertical spatiotemporal ground reaction force. A convolution neural network (CNN) is implemented to identify the features in the parkinsonian gaits and their progressive stages. Moreover, the variations of the gait pressures were visually recreated using ANSYS finite element software package. The CNN model has shown a 97% accuracy of recognizing parkinsonian gait and their different stages, and ANSYS model is implemented to visualize the pressure variation of the foot during a bottom-up approach.