<p>Parkinson’s disease (PD) typically manifests as speech dysarthria with articulatory and prosodic impairments. Linguistic diversity among the population poses a major challenge for developing generalizable diagnostic tools. This study aims to develop and assess an explainable artificial intelligence (XAI) framework capable of identifying language-independent, robust speech biomarkers for early PD detection. Four corpora of speech data (Italian, English, Colombian Spanish, and Castilian Spanish) and three speech tasks (reading passages, spontaneous speech, and text-dependent utterances) were analyzed. Eighty-nine acoustic features were extracted and processed through an optimized pipeline integrating feature selection (Lasso-CV, Random Forest, and Recursive Feature Elimination), Synthetic Minority Over-sampling Technique (SMOTE)-based data balancing, and hyperparameter tuning via GridSearchCV. Three classifiers, Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbors (KNN), were evaluated under monolingual, multilingual, and Leave-One-Language-Out (LOLO) cross-validation experiments. SHAP explainability analysis was applied to interpret the most influential acoustic biomarkers. The proposed approach achieved close to perfect performance (AUC values up to 100%) across corpora, in monolingual and multilingual experiments and 85% under LOLO validation, demonstrating reliable transferability to unseen languages. SHAP-based explainability revealed that prosodic (pitch variation, pause duration, and speech rate) and articulatory features, particularly Perceptual Linear Prediction (PLP) coefficients, were the most significant contribution for early PD diagnosis. The proposed XAI-driven multilingual pipeline identifies universal and language-specific speech biomarkers, enhancing diagnostic performance, interpretability, and generalization, paving the way for non-invasive, speech-based early screening of neurological disorders.</p>