Isotope tracing is a widely used technique to study metabolic activities by introducing heavy labeled nutrients into living cells and organisms. However, interpreting isotope tracing data is often heuristic, and application of automated methods using artificial intelligence is limited due to the paucity of evaluative knowledge. Our study developed a new pipeline that efficiently predicts metabolic activity in expansive metabolic networks and systematically quantifies flux uncertainty of traditional computational methods. We further developed an algorithm adept at significantly reducing this uncertainty, enabling robust evaluations of metabolic activity with limited data. Using this technology, we discovered highly reprogrammed mitochondria-cytosol exchange cycles in tumor tissue of patients, and observed similar metabolic patterns influenced by nutritional conditions in cancer cells. Thus, our refined methodology provides robust automated quantification of metabolism allowing for new insight into metabolic network activity.