Parkinson’s disease is neural-declining disease influencing 60% of the people above the age of 50 years This focuses on developing an automated Parkinson’s disease by using techniques of machine learning to analyze speech data. By extracting and analyzing unique vocal features such as jitter, shimmer, and harmonic-to-noise ratios from patient speech samples, etc., the model can identify subtle patterns associated with Parkinson’s. Using classification algorithms like Support Vector Machines (SVM) and Gradient Boosting, in combination with feature scaling and cross-validation, the model will achieve high accuracy in differentiating between individuals with and without THIS performance evaluation metrics uses accuracy, precision, recall, and F1-score are employed to ensure its robustness and reliability. The project could be extended with a simple interface for clinical use, offering an efficient, noninvasive diagnostic tool to support early detection. This approach demonstrates the potential of machine learning in medical diagnostics, paving the way for innovations in AI-driven healthcare that could improve patient care through the early and accurate diagnosis of neurological disorders.