Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the gradual loss of dopaminergic neurons in the brain, leading to both motor and non-motor symptoms. This study aims to develop an early diagnostic model for PD based on clinical data and biosignals, enabling timely intervention before significant neuronal damage occurs. Two machine learning algorithms, XGBoost and Random Forest, were employed to train and evaluate the performance of early PD classification on a dataset comprising 195 samples and 24 features. The features included voice data, early clinical indicators, and several non-invasive test results. The results demonstrated high performance for both models, with an accuracy of 0.92 and an F1-score of 0.95. XGBoost slightly outperformed Random Forest, achieving an AUC of 0.990 compared to 0.959, indicating superior class discrimination. The Ensemble model did not yield a significant performance improvement, likely due to the limited dataset size. This study highlights the potential of machine learning in supporting early diagnosis of Parkinson’s disease. It proposes a novel approach that shifts the treatment focus from symptom management to neuroprotection and damage prevention.