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Previous studies have relied on limited machine learning (ML) classifiers and voice attributes, which may introduce bias and hinder effective technique discovery. To address this, we developed an optimal PD prediction pipeline by testing multiple ML classifiers and feature extraction methods. We created the first Arabic PD speech dataset, comprising 40 subjects (17 with PD and 23 controls), and validated our methodology on an independent Spanish cohort of 100 subjects. Feature extraction included traditional, audio-to-text, and deep voice features from a pre-trained Whisper model. We employed feature selection and dimensionality reduction techniques to refine the dataset dimensions. Final features were assessed using twelve classifiers with leave-one-out and k-fold cross-validation for robust performance evaluation. Shapley additive explanations (SHAP) were utilized to determine feature importance as vocal biomarkers. 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