Worldwide, millions of peoples are affected by complex neuro degenerative disorder, that is known as PD (Parkinson Disease). In recent years, it is very critical for accurate diagnosis of PD at early stage for implementing timely interventions and improving patient life. There have been growing interest in developing intelligent systems for Parkinson’s disease detection, harnessing the potential of cutting-edge technologies such as artificial intelligence, machine l earning, and sensor-based data analysis. This research paper presents an in-depth exploration of an Intelligent Parkinson Disease Detection System (IPDDS) designed for early diagnosis and quality level monitoring of PD using two most accurate machine learning techniques—SVM and Random Forest. The presented work relates the use of various performance metrices like Accuracy, Confusion Matrix, Precision, etc. Standard Dataset having voice samples of human’s having Parkinson Disease and not having PD is used to validate the various performance parameters and predict the PD or non-PD.