This paper introduces a novel technique for Parkinson’s disease (PD) detection from speech samples utilizing Higher Order Dynamical Mode Decomposition (HODMD). By employing HODMD to extract dynamic features from the speech data of a patient or a healthy control, we seize intricate patterns inherent in the data. Our experimental effects exhibit the efficacy of the proposed technique as it should be discriminating among PD and non-PD instances, showcasing promising performance metrics. This progressive method offers a non-intrusive, green, and potentially fee-powerful means for early PD detection, thereby paving the manner for more desirable prognosis and intervention techniques in clinical settings. Additionally, it presents an exciting street for further exploration in biomedical signal processing and disorder detection methodologies.