Background: Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects millions of people worldwide, and is characterized by motor dysfunction and vocal impairment. A clinical challenge for early diagnosis remains the overlap of symptoms with other neurological conditions.

Objective: In this work, an intelligent hybrid machine learning (ML) framework is proposed to early predict PD by combining speech biomarker analysis and ensemble classification techniques.

Methods: We used the UCI Parkinson's dataset (197 instances, 22 speech features). A hybrid model of Random Forest (RF) and Support Vector Machine (SVM) with Recursive Feature Elimination (RFE) was developed and evaluated with 10-fold cross validation.

Results: The proposed hybrid model achieved an accuracy of 95.7%, precision of 0.96, recall of 0.94 and F1-score of 0.950, which outperforms all individual benchmark algorithms including deep learning approaches.

Conclusion: The hybrid ML framework shows a strong clinical potential to enable non-invasive early detection of Parkinson’s disease using speech biomarkers, providing a cost-effective screening tool.