Advancements integrating artificial intelligence with neurological diagnostics have led to the development of a fusion architecture leveraging semi-supervised speech embeddings for early detection of Parkinson’s disease. This approach captures subtle, complex acoustic changes in voice patterns—such as pitch and articulation variations—that precede motor symptom onset. By combining multiple machine learning layers and training on limited labeled data supplemented with extensive unlabeled samples, the system enhances robustness and generalizability across diverse patient profiles. This non-invasive diagnostic tool represents a promising avenue to facilitate earlier clinical intervention in Parkinson’s disease management.