The overall incidence of liver metastasis in colorectal cancer is as high as 50%, and surgery remains the only potentially curative approach for the metastatic disease. The recurrence rate of liver metastases within one year after surgery is still 60%-70% in clinical practice. Whether we can accurately predict the early recurrence of patients after surgery is one of the most important considerations in formulating the overall treatment strategy. In this study, we combined radiomics feature extraction with machine learning classification methods to develop a novel strategy for predicting intrahepatic metastases based on imaging radiomics and machine learning. We constructed and systematically evaluated multiple machine learning models to assess their performance. By validating these models on a test set, we determined the effectiveness of each predictive model and selected the one with the highest predictive accuracy. The integration of radiomics and machine learning methods demonstrated significant potential in predicting intrahepatic recurrence within one year after surgery in patients with colorectal cancer liver metastases. The Gradient Boosting, LightGBM, and Random Forest models all achieved classification accuracies (ACC) exceeding 65% across all classification tasks. Notably, the Random Forest model exhibited the best performance; while its classification accuracy was 65.52% in the imaging-only group, it increased to 75.86% when both imaging and clinical information were combined, with an area under the receiver operating characteristic curve (AUC) of 70.83%, indicating strong predictive capability. These findings suggest that these models have potential application value in supporting the diagnostic work of clinical radiologists, potentially helping to reduce workload and decrease the risk of misdiagnosis. The imaging omics model and the combined model have good predictive efficacy for the recurrence of colorectal cancer liver metastases within one year, and can be used to assist in the clinical stratification of postoperative patients and identify high-risk factors for poor prognosis.