Parkinson's disease (PD) is a brain disorder that causes speech and communication problems. The speech issues are often described as slow speech and difficulty with articulation because Jaw muscles don’t move with enough strength. Mainly, clinical experts make a voice assessment to analyze voice signals, to detect PD. In this paper, we designed an intelligent model based on empirical mode decomposition (EMD) techniques for the detection of Parkinson's. Firstly, the voice signals are passed through EMD. The suggested EMD -based model is then used to extract a collection of entropy features from voice data. The features that have been chosen are fed into a K-nearest neighbor (KNN) as well as another four classifiers least squares support vector machine (LS-SVM), bagged tree, SVM (support vector machine), and Kmeans for the comparison. The proposed model is evaluated using a publicly available dataset named UCI machine learning repository. Several tests were carried out, and the findings revealed that the proposed framework can classify voice signals with a 97% accuracy in the K-nearest neighbor (KNN) technique.