Parkinson’s Disease ranks as the second most prevalent neurodegenerative disorder globally, second only to Alzheimer’s Disease. Its impact is significant, affecting an estimated 7 to 10 million individuals worldwide. Notably, its incidence rises with age, typically manifesting after 50 years old. As the global population ages, the prevalence of Parkinson’s Disease is projected to escalate proportionately, presenting a considerable public health challenge. Presently, diagnosing Parkinson’s Disease remains challenging, lacking a definitive and universally applicable method. Therefore, there’s a growing interest in exploring alternative approaches, such as leveraging Machine Learning (ML) algorithms, particularly those trained on voice datasets, to enable early detection. Initial examination of available datasets reveals inherent imbalances, underscoring the need for meticulous preprocessing steps to ensure accurate and reliable analysis. In this study, a comprehensive investigation into various ML algorithms is proposed, incorporating a range of preprocessing techniques tailored to address dataset complexities. Notably, a Hybrid Classification System is suggested, integrating SMOTE to mitigate imbalance, feature selection algorithms to enhance predictive accuracy, and Ensemble methods to amalgamate diverse classifiers’ outputs, thereby optimizing diagnostic performance, achieving 98.3% accuracy.