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The project “Early Detection of Parkinson’s Disease Using Machine Learning” was developed with the aim of identifying Parkinson’s disease at an early stage through intelligent data analysis and predictive modeling techniques. The system analyzes patient-related data such as voice measurements, tremor patterns, and biomedical features to detect symptoms associated with Parkinson’s disease and assist healthcare professionals in early diagnosis. The application was developed using Python with a user-friendly interface using Streamlit, and machine learning algorithms such as Random Forest, Support Vector Machine (SVM), and Logistic Regression were used to achieve accurate prediction results. The system provides prediction outputs, graphical analysis, and confidence scores to improve understanding of the diagnostic process and support medical decision-making. This project highlights the importance of artificial intelligence in healthcare by providing a faster, cost-effective, and reliable solution for early disease detection while encouraging awareness, research, and technological innovation in medical applications