Given the high prevalence and relapse rates of hepatocellular carcinoma (HCC), an increased capacity for early identification of patients most at risk for post-resection recurrence would help improve patient outcomes and prioritize health care resources. Here, we combined spatial multi-transcriptomics and proteomics approaches to characterize the tumor and immunological landscape of 61 samples. We observed a spatial and HCC-recurrence-associated distribution of natural killer (NK) cells in the invasive front and tumor center. Using artificial-intelligence alongside an extreme gradient-boosting algorithm, we developed the Tumor Immune MicroEnvironment Spatial (“TIMES”) score based on the expression of five NK-associated markers (SPON2, ZFP36L2, ZFP36, VIM, and HLA-DRB1) to predict HCC recurrence. We also demonstrated that TIMES score (HR = 29.6, P < 0.001) outperforms the current standard tools for patient risk stratification including the TNM (HR = 1.93, P = 0.113) and BCLC (HR = 1.55, P = 0.253) systems. In the clinic, we validated the model in 103 patients from three multi-centered cohorts achieve a real-world sensitivity of 90.00% and specificity of 90.24%. In the lab, following up on the individual marker with the highest prediction accuracy, in vivo models revealed that SPON2 increases IFN-γ secretion and enhances infiltration potential of NK cells at the invasive front. Additionally, we established the TIMES score on a publicly accessible website that can be easily achieved by different levels of pathology labs to facilitate global prediction of HCC recurrence risk and stratification of high-risk patients. With its ability to efficiently stratify high-risk patients, it exemplifying the utility of artificial intelligence to improve our understanding on TIME features that underlie tumor progression.