Abstract: Radiomics-based early prediction and treatment efficacy evaluation is critical for personalized treatment strategies in patients with colorectal cancer liver metastases (CCLM). Owing to the high artificial intelligence (AI) participation, repeatability, and reliable performance, deep learning (DL) based on convolutional neural networks enhances the predictive efficacy of the models, enabling its potential clinical application more promising. Subsequent to the gradual construction of a multimodal fusion model and multicenter large sample database, radiomics and DL will become increasingly essential in the management of CCLM. This review focuses on the main steps of radiomics and DL, and summarizes the value of its application in early state prediction and treatment efficacy evaluation of different treatment modalities in CCLM, we also look forward to the potential of its in-depth application in the clinical management of CCLM.