In this paper, we present a comprehensive literature review focusing on machine learning (ML) and deep learning (DL) models utilized for Parkinson's disease (PD) detection. Spanning various methodologies, the survey encapsulates key studies employing a spectrum of algorithms beyond traditional SVM, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), ResNet models, Random Forest, Particle Swarm Optimization (PSO), and robust feature engineering techniques