Background: Anti-vascular endothelial growth factor (VEGF) monoclonal antibodies (mAbs) are widely used for tumor treatment, including metastatic colorectal cancer (mCRC). So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab. Methods: 15 German and Austrian centers prospectively recruited 154 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. Plasma samples were collected every two weeks until radiologic progression (RECIST 1.1) as determined by CT scans performed every 2 months. 102 pre-selected CAFs were centrally analyzed using a cytokine multiplex assay (Luminex, Myriad RBM). Results: Using random forest machine learning, we developed a predictive model that discriminated between the situations of ”no progress within 100 days before radiological progress” and ”progress within 100 days before radiological progress”. Into this we incorporated a combination of ten out of the 102 CAF markers, which fulfilled this task with 81% accuracy, 72% sensitivity, and 88% specificity. Conclusions: Using artificial intelligence we identified a CAF marker combination that indicates treatment resistance to FOLFOX plus bevacizumab in patients with mCRC within 100 days prior to radiologic progress. Further studies are required to show its clinical value. Clinical trial information: NCT02331927.