Researchers have pioneered a fusion architecture combining multiple machine learning models employing semi-supervised speech embeddings to detect Parkinson’s disease through subtle vocal alterations. This method captures nuanced acoustic features such as pitch and articulation changes preceding hallmark motor symptoms. The semi-supervised paradigm enhances robustness despite scarce labeled clinical data by leveraging extensive unlabeled speech samples. This advancement offers a non-invasive, scalable diagnostic tool that could significantly refine early detection and monitoring approaches in neurodegenerative care.