There were several attempts to diagnose laryngeal cancer using machine learning (ML). A study by Aicha extracted acoustic features from recorded speech and performed feature selection to determine which speech features were most important for diagnosis. Another study by Z. Li and colleagues. Analyzed the Raman spectra from normal larynxes and cancerous larynxes and used a feature extraction approach to identify which parts of the Raman spectra indicated presence of a tumor . Finally, a study by Singh and Maurya proposed a computer-aided-diagnosis system which can analyze patches of endoscopic videos, perform feature extraction, and determine which parts of an endoscopy are most crucial for diagnosis . However, there is no existing research that uses biological attributes of microRNA for laryngeal cancer diagnosis. Thus, we propose a novel ML-based approach that can use biomarkers of cancer-associated miRNAs. This approach is far less invasive and more accessible, and it has the potential to improve diagnostics.