Mortality in colorectal cancer (CRC) remains high, resulting in 860,000 deaths annually. Carcinoembryonic antigen is widely used in clinics for CRC patient follow-up, despite carrying a limited prognostic value. Thus, an obvious need exists for multivariate prognostic models. We analyzed 48 biomarkers using a multiplex immunoassay panel in preoperative serum samples from 328 CRC patients who underwent surgery at Helsinki University Hospital between 1998 and 2003. We performed a multivariate prognostic forward-stepping background model based on basic clinicopathological data, and a multivariate machine-learned prognostic model based on clinicopathological data and biomarker variables, calculating the disease-free survival using the value of importance score. From the 48 analyzed biomarkers, only IL-8 emerged as a significant prognostic factor for CRC patients in univariate analysis (HR 4.88; 95% CI 2.00-11.92; p = 0.024) after correcting for multiple comparisons. We also developed a multivariate model based on all 48 biomarkers using a random survival forest analysis. Variable selection based on a minimal depth and the value of importance yielded two tentative candidate CRC prognostic markers: IL-2Ra and IL-8. A multivariate prognostic model using machine-learning technologies improves the prognostic assessment of survival among surgically treated CRC patients.