Parkinson’s disease (PD) is a brain dysfunction condition that affects thousands of people across the globe. Early detection of Parkinson’s disease is crucial for effective treatment and control of symptoms. This study employs a multi-modal methodology coded as ParkinSense that combines spiral drawings, gait analysis, and Magnetic Resonance Imaging (MRI) analysis for timely Parkinson’s disease identification. MRI analysis provides insight into structural and functional changes in the brain, revealing potential anomalies. GAIT analysis assesses alterations in walking patterns, such as reduced arm swing and slow gait velocity. Spiral drawings, serve as a surrogate for motor dexterity and fine motor control, both of which are compromised in Parkinson’s disease. In this study, data from Parkinson’s patients and healthy candidates are collected and analyzed using advanced machine-learning techniques to create models for accurate disease prediction and classification. Specifically, the gait analysis is conducted using a Support Vector Machine and XGBoost algorithm. The spiral drawings are analyzed using a Convolutional Neural Network. The MRI data is processed using a MobileNet-based neural network. These algorithms are integrated to create models for accurate disease prediction and classification. The results demonstrate that this multimodal technique is equipped to identify Parkinson’s disease with an accuracy rate of more than 90%. Early detection using speech analysis, gait analysis, and spiral drawings can lead to earlier intervention and treatment, resulting in a better excellence of life for those affected. This study adds to the expanding body of knowledge about non-invasive, cost-effective approaches for the timely identification of Parkinson’s disease.