The aim of our study was to develop and validate a machine learning algorithm to predict response of individual HER2‐amplified colorectal cancer liver metastases (lmCRC) undergoing dual HER2‐targeted therapy. Twenty‐four radiomics features were extracted after 3D manual segmentation of 141 lmCRC on pretreatment portal CT scans of a cohort including 38 HER2‐amplified patients; feature selection was then performed using genetic algorithms. lmCRC were classified as nonresponders (R−), if their largest diameter increased more than 10% at a CT scan performed after 3 months of treatment, responders (R+) otherwise. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values in correctly classifying individual lesion and overall patient response were assessed on a training dataset and then validated on a second dataset using a Gaussian naïve Bayesian classifier. Per‐lesion sensitivity, specificity, NPV and PPV were 89%, 85%, 93%, 78% and 90%, 42%, 73%, 71% respectively in the testing and validation datasets. Per‐patient sensitivity and specificity were 92% and 86%. Heterogeneous response was observed in 9 of 38 patients (24%). Five of nine patients were carriers of nonresponder lesions correctly classified as such by our radiomics signature, including four of seven harboring only one nonresponder lesion. The developed method has been proven effective in predicting behavior of individual metastases to targeted treatment in a cohort of HER2 amplified patients. The model accurately detects responder lesions and identifies nonresponder lesions in patients with heterogeneous response, potentially paving the way to multimodal treatment in selected patients. Further validation will be needed to confirm our findings. What's new? Therapies targeting human epidermal growth factor receptor 2 (HER2) have shown promise in patients with metastatic colorectal cancer (CRC) with HER2 amplification. Predicting which CRC metastases respond to HER2‐targeted therapy could significantly advance personalized treatment in this patient subpopulation. Here, employing a machine learning method, the authors describe a novel radiomics signature capable of predicting liver metastasis response to HER2‐targeted therapy. The model correctly identified non‐responder lesions in patients with heterogeneous therapeutic response. The ability of the radiomics signature to identify HER2 therapy‐responsive liver metastases could facilitate the generation of more sophisticated diagnostic and therapeutic strategies in select CRC patients.