Gutter oil, a major public health concern in East Asia, is often indistinguishable from pure edible oils using conventional physical and chemical methods. In this study, we present a novel approach for detecting gutter oil using microRNAs (miRNAs) as biomarkers. We proved that miRNAs exist in edible oils and can be used to differentiate between pure and recycled oils. A combination of qRT-PCR and machine learning techniques was employed to characterize miRNA profiles across commercial vegetable oils, animal oils, and gutter oil. Specifically, the relative abundances of miR-16 and let-7a were found to be significantly different among these oils, allowing for accurate differentiation via a support vector machine (SVM) model. The results indicate that miRNAs such as miR-16 and let-7a serve as reliable biomarkers, enabling classification of gutter oil even when it complies with national standards. This research provides a feasible and effective method for detecting gutter oil, with potential implications for improving food safety and public health.