With　the　growing　global　challenge　of　drug　abuse,　there　is　an　urgent　need　for　rapid,　accurate,　and　cost-effective　drug　detection　methods.　This　study　introduces　an　innovative　approach　to　drug　abuse　screening　by　quickly　detecting　ephedrine　(EPH)　in　tears　using　drop　coating　deposition-surface　enhanced　Raman　spectroscopy　(DCD-SERS)　combined　with　machine　learning　(ML).　Using　ultra　performance　liquid　chromatography-tandem　mass　spectrometry　(UPLC-MS/MS),　the　average　concentration　of　EPH　in　tear　fluid　of　Sprague-Dawley　(SD)　rats,　measured　over　3　h　post-injection,　was　1235　ng/mL.　DCD-SERS　effectively　identified　EPH　in　tear　samples,　with　distinct　Raman　peaks　observed　at　1001　cm-1　and　1242　cm-1.　To　enable　rapid　analysis　of　complex　SERS　data,　three　ML　algorithms-linear　discriminant　analysis　(LDA),　partial　least　squares　discriminant　analysis　(PLS-DA),　and　random　forest　(RF)-were　employed.　These　algorithms　achieved　over　90%　accuracy　in　distinguishing　between　EPH-injected　and　non-injected　SD　rats,　with　area　under　the　ROC　curve　(AUC)　values　ranging　from　0.9821　to　0.9911.　This　approach　offers　significant　potential　for　law　enforcement　by　being　easily　accessible,　non-invasive　and　ethically　appropriate　for　examinees,　while　being　rapid,　accurate,　and　affordable　for　examiners.