The neurological system, the physiological system, and the behavioral system of the brain are impacted by a medical condition called Parkinson’s disease (PD), which presents vague symptoms in its initial stages, posing challenges in its accurate diagnosis. The “bradykinesia,” which means slow movement, is one of the usual symptoms of this illness. Detecting Parkinson’s early lowers risks of death and allows for a more accurate diagnosis. Different algorithms such as Support Vector Machines (SVM), Naïve Bayes Classifier, Gradient Boosting Machines (GBM), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Random Forest (RF), and XG Boost are used to examine the result with reference to Accuracy, Execution Time, F1-Score, Sensitivity, and Specificity. The Parkinson’s Telemonitoring Voice Dataset is utilized in this research study to track and examine how Parkinson’s disease is developing using voice assessments. The outcomes of the suggested ANN model show increased Accuracy (96.7%), Execution Time (25 ms), F1-Score (87.01%), Sensitivity (92.42%), and Specificity (93.54%), were positive.