Voice technology has advanced significantly in the recent years, not only in itself but also using it as a biomarker for medical and research purposes has proven its benefits. It served as a biomarker to detect Parkinson’s disease (PD) in the subject. PD is a neurological, chronic disease that progressively decays the body of the patient. It hampers the most basic human functions such as movement, speech, and eyesight. As it is a disease that usually shows its symptoms late in human life, its detection becomes crucial. Although there is no cure to the disease, once detected early patients can search for alternative medicines and treatment, and can even delay the symptoms by the early diagnosis. Analyzing the speech and other voice-related attributes of a person leads to the o detection of any early signs or symptoms of Parkinson’s. As speech impediment is one of the most evident symptoms of Parkinson’s patients, can be an important resource for early detection. The model displayed the following data: average F1-score of 93.94%, average recall of 94%, average precision of 93.5%, and accuracy of 93.3%. In the future, further work is to be done to increase the overall accuracy of this machine learning model, to provide the best and more precise outcomes.