Parkinsonism exemplifies a ubiquitous ailment encompassing neuron related deterioration in other words, recurring encephalic decline related ailment that emerges via motile impairments (mobility restrictions) and brain related issues (cognitive), symptoms often echoing embodiments concerning aging related afflictions or frailties. Regrettably, Parkinson's Disease has no solution, consequently, patients experiencing Parkinson's count on premature diagnosis and personalized therapies to decelerate t he advancement of the affliction. PD is possible to be addressed via prompt identification, as a result facilitating healthcare recipients to maintain standard living. Considering initial symptoms are non aggressive, numerous sufferers persist unacknowledg ed prior to the time when the manifestations develop extremely in addition the intervention progresses substantially arduous. These conditions commence sporadically and incrementally transform uninterrupted while the disorder evolves. Preliminary indicator s concerning this ailment constitute for instance, patients perhaps perceive moderate trembling or experience struggle standing up a chair or sitting place. Parkinsonism exhibits 5 phases involving advancement plus 90% PD demonstrate evidence related to vo cal fold lesions being an indication of initial phase. Because of these individuals possibly encounter communication obstacles, for instance garbled vocalization, struggle enunciating, as well as communicating extremely at a slow pace or rapidly. The afore mentioned obstacles are recognized as dysarthria (verbal motor impairment) furthermore is able to be triggered as a result of muscle fibres utilized in articulation unable to perform adequately, promptly, otherwise with ample force. This research paper emp hasizes the implementation pertaining to (ML) machine learning strategies regarding the articulation datasets for the purpose of identifying Parkinsonism throughout its preliminary phases. The (ML) machine learning approaches highlighted within the researc h paper include Support Vector Machine (SVM), Random Forest (RF), K Nearest Neighbours’ (KNN) and AdaBoost including strategies aimed at optimizing the hyperparameters in order to elevate overall accuracy. This paper further displays comparative analysis p ertaining to these four (ML) machines learning methodologies