The illegal wildlife markets of Southeast Asia are bolstered by organised criminal networks and the region's rich density of charismatic wildlife. Forensic tools identifying species and their origins are vital to combat wildlife crime. However, many require expensive technology and skilled personnel, limiting their use in rural trade hotspots. This study introduces a replicable statistical framework, using skull morphometrics, to distinguish related species with simple measurements. We developed a web-based classifier trained on clouded leopard (Neofelis spp.) skulls from museum collections across Europe, Asia and the U.S.A., a genus often targeted in wildlife trade. Our categorical predictive model, based on two key metrics, the fronto-nasal "pit" and m1 talonid morphology achieved 97% accuracy (p