Hand gestures have evolved into a natural and intuitive means of engaging with technology. Parkinson’s disease is a neurological ailment that worsens with time. Sophisticated deep learning-based system for the precise identification of Parkinson’s Disorder is developed through the interpretation and analysing of hand gestures exhibited by affected individuals. Relevant features are extracted from the images, employing computer vision techniques, enabling the analysis of hand movements. A deep learning model is trained to recognize distinctive motor symptoms associated with the disorder. Object detection algorithms are employed to pinpoint specific deviations in finger and hand positions, thereby quantifying tremors, bradykinesia, and other relevant motor symptoms. The approach lies in the utilisation of a dataset comprising 40 images for each Parkinson’s hand gesture, enabling robust model training. The Mobilenet SSD algorithm for object detection to find the specific deviations in finger and hand positions. The model exhibits a high level of accuracy in distinguishing between individuals with the possibility of Parkinson’s disorder and those without, based on the subtle motor symptoms exhibited in their hand gestures.