Parkinson’s disease, which affects millions of people worldwide, is a term used to describe a neurological and neurodegenerative movement disorder. Common symptoms include a loss of automatic motions and muscle rigidity, which ultimately result in problems with balance, coordination, and walking. The patient’s physical, emotional, and mental health gradually worsens as a result of these symptoms. Before the patient’s health worsens, therapeutic care can be given to lower the disease’s prognosis. It is possible to predict whether or not a person has Parkinson’s disease using machine learning classification algorithms. This can lengthen the lives of older individuals and improve their quality of life when they have Parkinson’s. This study suggests a potential technique to identify Parkinson’s disease symptoms in their early stages. Based on the speech input parameters, algorithms like Gradient Boosting, XGBoost, Random Forest, and Extra Trees Classification are used to estimate whether the individual is normal or affected by Parkinson’s disease. According to this study, the ensemble method Gradient Boosting classification algorithm outperformed other classification algorithms in terms of test accuracy rate (95%). The effectiveness of the approaches was evaluated using a reliable dataset from the UCI Machine Learning library.