Parkinson's disease (PD) has an incidence of 15 to 43 per one lac population, an estimate showing that India has more than one lac PD patient and is expected to have the largest number of PD patients in the world. About 40-45% of the patients have had their initial motor manifestation at the age of 22-49, which is known as Early Onset Parkinson's Disease (EOPD) (Early et al. (EOPD) in India Vs. Western Populations, n.d.)1. The research aims to harness the power of artificial intelligence and machine learning to develop a predictive model for diagnosing Parkinson's disease (PD). This initiative aligns with the growing potential of Artificial Intelligence (AI) in healthcare research, particularly in addressing classification challenges like PD diagnosis. By leveraging advanced algorithms and data analysis techniques, this study enhances early prediction of PD, facilitating timely intervention and improving patient outcomes. The zenith of the study is marked by the K-Nearest-Neighbors (KNN) algorithm with the highest accuracy score of 97.44%, the greatest power to judge procedure, and the Kappa statistic of 90.78%, which explains the highest level of diagnostic concordance. The Stacking of Random Forest, KNN, and AdaBoost produces 100% specificity and f1 score. Also, these two algorithms achieved an ROC AUC score of 100%, thus clinching ground in the contest of the precision of a discriminating model. Contrarily, the performance of the Naïve Bayes classifier is lower in all performance metrics. The facts retrieved in this study lead to the bewildering benefit of ensemble and KNN algorithms in forecasting Parkinson's disease in advance. It may lead to a revolutionary turnaround in patient care and therapeutic approaches.¹Early onset parkinsonism (EOPD) in India vs. Western populations.