This study proposes an ensemble machine learning (ML) approach for the early detection of Parkinson's disease (PD) using speech signals. The model employs the AdaBoost classifier, trained on a dataset from the University of California, Irvine (UCI) Machine Learning Repository, which includes various voice attributes such as time-frequency features, Mel frequency cepstral coefficients (MFCCs), wavelet transform features, vocal fold features, and tremor waveform quality time (TWQT). The model demonstrated high performance, achieving accuracy, precision, recall, F1 score, and AUC of 0.96, 0.98, 0.93, 0.95, and 0.99, respectively. The robustness of the model was validated through cross-validation, showing consistent performance across iterations. The study addresses the limitations of traditional diagnostic methods by leveraging non-motor symptoms, particularly speech impairments, for early detection. The proposed model is non-invasive, cost-effective, and can aid healthcare professionals in timely diagnosis and intervention. The study also highlights the importance of using advanced pre-processing techniques, such as SMOTE and PCA, to handle imbalanced datasets and improve model accuracy. The results indicate that the proposed model outperforms existing ML and deep learning approaches in PD detection, demonstrating its potential as a reliable and effective diagnostic tool. The study contributes to the field by providing a robust system for PD detection using speech signals, emphasizing the role of ML in advancing diagnostic techniques for neurodegenerative diseases.This study proposes an ensemble machine learning (ML) approach for the early detection of Parkinson's disease (PD) using speech signals. The model employs the AdaBoost classifier, trained on a dataset from the University of California, Irvine (UCI) Machine Learning Repository, which includes various voice attributes such as time-frequency features, Mel frequency cepstral coefficients (MFCCs), wavelet transform features, vocal fold features, and tremor waveform quality time (TWQT). The model demonstrated high performance, achieving accuracy, precision, recall, F1 score, and AUC of 0.96, 0.98, 0.93, 0.95, and 0.99, respectively. The robustness of the model was validated through cross-validation, showing consistent performance across iterations. The study addresses the limitations of traditional diagnostic methods by leveraging non-motor symptoms, particularly speech impairments, for early detection. The proposed model is non-invasive, cost-effective, and can aid healthcare professionals in timely diagnosis and intervention. The study also highlights the importance of using advanced pre-processing techniques, such as SMOTE and PCA, to handle imbalanced datasets and improve model accuracy. The results indicate that the proposed model outperforms existing ML and deep learning approaches in PD detection, demonstrating its potential as a reliable and effective diagnostic tool. The study contributes to the field by providing a robust system for PD detection using speech signals, emphasizing the role of ML in advancing diagnostic techniques for neurodegenerative diseases.