Purpose:　The　objective　of　this　study　was　to　identify　the　risk　factors　associated　with　Carbapenem-resistant　Enterobacteriaceae　(CRE)　colonization　in　intensive　care　unit　(ICU)　patients　and　to　develop　a　predictive　risk　model　for　CRE　colonization.　Patients　and　Methods:　In　this　study,　121　ICU　patients　from　Fujian　Provincial　Hospital　were　enrolled　between　January　2021　and　July　2022.　Based　on　bacterial　culture　results　from　rectal　and　throat　swabs,　patients　were　categorized　into　two　groups:　CRE-colonized　(n　=　18)　and　non-CRE-colonized　(n　=　103).　To　address　class　imbalance,　Synthetic　Minority　Over-sampling　Technique　(SMOTE)　was　applied.　Statistical　analyses　including　T-tests,　Chi-square　tests,　and　Mann-Whitney　U-tests　were　employed　to　compare　differences　between　the　groups.　Feature　selection　was　performed　using　Lasso　regression　and　Random　Forest　algorithms.　A　Logistic　regression　model　was　then　developed　to　predict　CRE　colonization　risk,　and　the　results　were　presented　in　a　nomogram.　Results:　After　applying　SMOTE,　the　dataset　included　198　CRE-colonized　patients　and　180　non-CRE-colonized　patients,　ensuring　balanced　groups.　The　two　groups　were　comparable　in　most　clinical　characteristics　except　for　diabetes,　previous　emergency　department　admission,　and　abdominal　infection.　Eight　independent　risk　factors　for　CRE　colonization　were　identified　through　Random　Forest,　Lasso　regression,　and　Logistic　regression,　including　Acute　Physiology　and　Chronic　Health　Evaluation　(APACHE)　II　score　＞　16,　length　of　hospital　stay　＞　31　days,　female　gender,　previous　carbapenem　antibiotic　exposure,　skin　infection,　multi-site　infection,　immunosuppressant　exposure,　and　tracheal　intubation.　The　risk　prediction　model　for　CRE　colonization　demonstrated　high　accuracy　(87.83%),　recall　rate　(89.9%),　precision　(85.6%),　and　an　AUC　value　of　0.877.　Patients　were　categorized　into　low-risk　(0-90　points),　medium-risk　(91-160　points),　and　high-risk　(161-381　points)　groups,　with　corresponding　CRE　colonization　rates　of　1.82%,　7.14%,　and　58.33%,　respectively.　Conclusion:　This　study　identified　independent　risk　factors　for　CRE　colonization　and　developed　a　predictive　model　for　assessing　the　risk　of　CRE　colonization.