Parkinson’s disease (PD) and other neurological disorders pose significant global health challenges. Despite technological advancements, early diagnosis of PD remains difficult. This review emphasizes the importance of machine learning techniques in aiding medical professionals with precise and rapid PD detection. It compares computational methods used for PD diagnosis, focusing on the effectiveness of classification algorithms. Multiple strategies have been applied to improve diagnostic accuracy, but selecting the most efficient classifier is complex due to varying performance across datasets. Using a voice dataset from the UCI library, this study assesses three classifiers: multilayer perceptron (MLP), K-nearest neighbor (KNN), and support vector machine (SVM). The findings demonstrate that an artificial neural network (ANN) using the Levenberg–Marquardt method outperforms other classifiers and attains the best accuracy, exceeding 96%. These results highlight how ANNs can improve early PD diagnosis and enabling timely therapeutic interventions.