This study proposes an ensemble machine learning (ML) approach for the early detection of Parkinson’s disease (PD) using speech signals. The AdaBoost classifier is utilized to construct the model, trained on a dataset from the University of California, Irvine (UCI) repository. The dataset includes various voice attributes such as time-frequency features, Mel frequency cepstral coefficients, wavelet transform features, vocal fold features, and tremor waveform quality time. The model demonstrates promising performance, achieving high accuracy, precision, recall, F1 score, and AUC score of 0.96, 0.98, 0.93, 0.95, and 0.99, respectively. Cross-validation ensures consistent performance across all iterations. The study aims to contribute to the scientific community by providing a robust system for PD detection, leveraging the non-invasive and cost-effective nature of speech analysis. The proposed model addresses the limitations of traditional diagnostic methods, which often rely on motor symptoms and may not detect PD until later stages. By incorporating non-motor symptoms, the model enriches the diagnostic process, facilitating timely intervention and improving patient outcomes. The study also highlights the importance of advanced pre-processing techniques, such as SMOTE and PCA, to handle imbalanced datasets and reduce dimensionality. The ensemble learning approach, specifically AdaBoost, enhances the model's performance and generalization ability, making it well-suited for complex speech datasets. The results show superior performance compared to existing ML and deep learning models, demonstrating the potential of ML in PD detection using speech signals.This study proposes an ensemble machine learning (ML) approach for the early detection of Parkinson’s disease (PD) using speech signals. The AdaBoost classifier is utilized to construct the model, trained on a dataset from the University of California, Irvine (UCI) repository. The dataset includes various voice attributes such as time-frequency features, Mel frequency cepstral coefficients, wavelet transform features, vocal fold features, and tremor waveform quality time. The model demonstrates promising performance, achieving high accuracy, precision, recall, F1 score, and AUC score of 0.96, 0.98, 0.93, 0.95, and 0.99, respectively. Cross-validation ensures consistent performance across all iterations. The study aims to contribute to the scientific community by providing a robust system for PD detection, leveraging the non-invasive and cost-effective nature of speech analysis. The proposed model addresses the limitations of traditional diagnostic methods, which often rely on motor symptoms and may not detect PD until later stages. By incorporating non-motor symptoms, the model enriches the diagnostic process, facilitating timely intervention and improving patient outcomes. The study also highlights the importance of advanced pre-processing techniques, such as SMOTE and PCA, to handle imbalanced datasets and reduce dimensionality. The ensemble learning approach, specifically AdaBoost, enhances the model's performance and generalization ability, making it well-suited for complex speech datasets. The results show superior performance compared to existing ML and deep learning models, demonstrating the potential of ML in PD detection using speech signals.