Parkinson′s disease (PD) is a progressive neurological disorder characterized by motor and non-motor symptoms resulting from the degeneration of dopamine-producing neurons in the brain′s substantia nigra. Common motor symptoms include tremors, rigidity, postural instability, and bradykinesia. While PD has no cure, medications can help manage symptoms. Early detection is crucial for timely intervention. This paper surveys datasets and machine learning (ML) approaches for PD detection, comparing their effectiveness. ML models like Artificial Neural Networks (ANN), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and XGBoost have been employed. ANN and XGBoost achieved accuracies of 96.7% and 95%, respectively, using extensive voice datasets, while SVM attained 99% accuracy with smaller datasets. Parkinson′s disease, a progressive neurological disorder, manifests motor symptoms due to dopamine neuron degeneration. Medications can mitigate symptoms, emphasizing the importance of early detection. This study evaluates ML techniques for PD detection, highlighting ANN, SVM, KNN, and XGBoost. ANN and XGBoost excel with large voice datasets, achieving 96.7% and 95% accuracy, respectively. SVM outperforms with smaller datasets, achieving 99% accuracy. Early prediction facilitated by ML classification models is crucial for timely intervention in Parkinson′s disease management.