A neurological disorder known as Parkinson's Disease (PD) affects 60% of the population over the age of 50. Individuals with Parkinson's encounter movement impairments and communication obstacles, rendering physical appointments for treatment and monitoring problematic. Freezing of Gait (FOG) is extremely weakening yet inadequately comprehended sign of PD. FOG is an irregular gait pattern marked via incapability to initiate steps or turn when ambulating, especially in the sense of constricted environments. This syndrome compromises equilibrium, elevates the incidence of falls and diminishes quality of life. This FOGPD can be treated early, remotely, and correctly. Accurate diagnosis of PD necessitates robust Machine Learning (ML) and Deep Learning (DL) techniques together with effective medical instruments for evaluating neurological health. This research provides three machine learning strategies utilizing a hybrid model to detect partial discharge in its early phases. The results are contrasted with three applied ML techniques: Gradient Boosting, Decision Tree Random Forest along side a proposed Hybrid algorithm, which integrates Random Forest and Gradient Boosting.