Current Parkinson’s Disease (PD) diagnostic methods, such as Magnetic Resonance Imaging (MRI), Positron emission tomography (PET), and Single Photon Emission Computed Tomography (SPECT) scans, require extensive resources, generate significant waste, and rely on energy-intensive procedures, contributing to high operational costs and environmental impact. These current PD diagnostic methods produce various types of lean waste, including excess inventory of disposable materials, overproduction of redundant tests, defects due to misdiagnoses requiring repeat scans, motion waste from patient movement between departments, and long waiting times for scheduling and processing. This research examines the inefficiencies inherent in current PD diagnostic methods and evaluates the potential sustainability benefits of a novel voice recognition-based device as an alternative. The findings show that the novel method significantly reduces various types of waste, such as excess inventory, overproduction, and over-processing … etc. It offers a non-invasive, cost-effective alternative with minimal infrastructure needs, making it particularly beneficial in resource-limited settings by improving diagnostic accessibility and speeding up patient care. The study underscores the potential of integrating advanced technology using lean principles to promote environmentally and socioeconomic responsible healthcare innovations.