Parkinson’s disease (PD) is a neurodegenerative disorder that affects movement, speech, and other functions. Dysarthria is a speech disorder often associated with PD. It can reduce intelligibility, making it difficult for people with PD to communicate. In recent years, there has been a growing interest in the use of machine learning (ML) and artificial intelligence (AI) to diagnose this disorder. This study proposes an automated system for classifying the severity of dysarthria in patients with PD using a combination of speech signal analysis, ML algorithms, and voting techniques. This study uses a variety of features extracted from the speech signal, including statistical signal characterization (SSC), Mel-frequency cepstral coefficients (MFCCs), and discrete packet wavelet decomposition (DPWD). These features are used to train ML models, such as support vector machines (SVM), k-nearest neighbors (KNN), neural networks (NN), and convolutional neural networks (CNN). Two versions of the system were implemented: one using the National Technical Institute of the Deaf (NTID) scale, which classifies the severity of dysarthria into five levels, and a simpler version that classifies the severity into three levels: mild, moderate, and severe. Several approaches were explored to improve the test accuracy, including segmenting the subjects, and voting on different levels. The results of this project can assess the treatment of speech disorders in patients with PD and improve the success of the speech therapies. The results of the evaluation showed that the system was able to classify the severity of dysarthria using DPWD and hard voting between KNN, SVM, and NN with an accuracy of 90.5% using Segment-Subject-Model voting on the data collected by the hospital of the university of Kiel, Germany.