Surface-Enhanced　Raman　Spectroscopy　(SERS)　combined　with　machine　learning　offers　a　transformative　label-free　approach　for　colorectal　cancer　detection,　addressing　limitations　of　current　diagnostic　methods　such　as　invasiveness,　high　cost,　and　limited　accessibility.　In　this　study,　we　developed　a　liquid　biopsy　platform　utilizing　a　bacterial　cellulose　substrate　functionalized　with　silver　nanoparticles,　synthesized　via　an　ascorbic　acid　cycling　reduction　method,　to　enhance　SERS　signals　from　plasma　without　the　need　for　complex　reagents.　The　study　enrolled　20　colorectal　cancer　patients　and　20　healthy　volunteers,　and　plasma　SERS　spectra　were　collected　from　each　participant.　To　improve　reproducibility　and　minimize　environmental　interference,　4-mercaptopyridine　was　introduced　as　an　internal　standard　for　signal　calibration.　Machine　learning　models—including　Decision　Tree　(DT),　K-Nearest　Neighbors　(KNN),　Random　Forest　(RF),　and　Support　Vector　Machine　(SVM)—achieved　classification　accuracies　all　exceeding　86.25　%,　with　SVM　reaching　100　%　accuracy　in　distinguishing　between　colorectal　cancer　patients　and　healthy　individuals.　This　study　establishes　a　clinically　promising　SERS–machine　learning　framework　that　integrates　standardized　calibration　with　interpretable　artificial　intelligence,　facilitating　rapid　cancer　screening　and　advancing　precision　oncology.　It　should　be　noted,　however,　that　these　findings　are　based　on　a　limited　sample　size,　and　further　validation　in　larger　cohorts　is　essential.　©　2025