This research explores the potential of machine learning (ML) in identifying vocal markers for schizophrenia. While previous research showed that voice-based ML models can accurately predict schizophrenia diagnosis and symptoms, it is unclear to what extent such ML markers generalize to different clinical subpopulations and languages: the assessment of generalization performance is however crucial for testing their clinical applicability. We systematically examined voice-based ML model performance on a large cross-linguistic dataset (3 languages: Danish, German, Chinese). Employing a rigorous pipeline to minimize overfitting, including cross-validated training sets and multilingual models, we assessed generalization on participants with schizophrenia and controls speaking the same or different languages. Model performance was comparable to state-of-the art findings (F1-score ~ 0.75) within the same language; however, models did not generalize well - showing a substantial decrease - when tested on new languages, and the performance of multilingual models was also generally low (F1-score ~ 0.50).