Parkinson’s disease (PD) is a degenerative neurological disorder that impairs a person’s capacity to perform daily activities. Both motor and non-motor symptoms, including tremors, muscle stiffness, slowed movements, speech problems, balance problems, and changes in handwriting, are caused by the gradual disruption of nerve cell communication. Better patient outcomes and prompt treatment depend on early detection; however, because early symptoms are so varied and subtle, diagnosis frequently happens later. Fascinatingly, early Parkinson’s disease (PD) can result in subtle abnormalities in speech that may be inaudible to the human ear but can be identified through acoustic analysis. This project develops a robust predictive model for classifying individuals as PD patients or healthy controls using biomedical voice recordings and related features. The dataset consists of 754 features extracted from voice and biomedical signals, presenting challenges related to high dimensionality and computational complexity. To address these challenges, the paper employs a hybrid approach combining traditional and advanced machine learning algorithms with ensemble learning strategies to improve classification accuracy and generalizability. Feature selection and dimensionality reduction techniques are applied to identify the most relevant attributes, reduce noise, and enhance model interpretability. Experimental results demonstrate that the proposed ensemble-based model effectively detects PD with high accuracy of 97.96%, precision, and recall of 0.83%,0.65% respectively, offering a valuable data driven tool to assist healthcare professionals in the early diagnosis of Parkinson’s Disease.