Parkinson's disease (PD) is a progressive neurodegenerative disorder that significantly impacts the quality of life of millions worldwide. Early detection is crucial for effective management and improved patient outcomes. This study aims to compare the efficacy of various machine learning algorithms in detecting Parkinson's disease using a comprehensive dataset of clinical and biometric features. We evaluate and compare the performance of several machine learning algorithms, including Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks (ANN), and Gradient Boosting Machines (GBM), in classifying PD cases. The study utilizes a diverse set of features, including voice recordings, gait analysis, and neuroimaging data. Results demonstrate the potential of machine learning in enhancing PD diagnosis, with [specific algorithm] showing the highest accuracy of [X%]. This comparative analysis provides insights into the strengths and limitations of different algorithms, paving the way for more robust and reliable PD detection methods.