SummaryTrade restrictions for endangered elasmobranch species exist to disincentivise their exploitation and curb their declines. However, trade monitoring is challenging due to product variety and the complexity of import/export routes. We investigate the use of a portable, universal, DNA-based tool which would greatly facilitatein-situmonitoring. We collected shark and ray samples across the Island of Java, Indonesia, and selected 28 commonly encountered species (including 22 CITES-listed species) to test a recently developed real-time PCR single-assay originally developed for screening bony fish. In the absence of a bespoke elasmobranch identification online platform in the original FASTFISH-ID model, we employed a deep learning algorithm to recognize species based on DNA melt-curve signatures. By combining visual and machine-learning assignment methods, we distinguished 25/28 species, 20 of which were CITES-listed. With further refinement, this method can improve monitoring of the elasmobranch trade worldwide, without a lab or species-specific assays.