Non-alcoholic fatty liver disease (NAFLD) is affecting more people globally. Indeed, NAFLD is a spectrum of metabolic dysfunctions that can progress to hepatocellular carcinoma (NAFLD-HCC). This development can occur in a non-cirrhotic liver and thus, often lack clinical surveillance. The aim of this study was to develop non-invasive surveillance method for NAFLD-HCC.Using comprehensive ultra-high-performance liquid chromatography mass-spectrometry, we investigated 1,295 metabolites in serum from 249 patients. Area under the receiver operating characteristic curve was calculated for all detected metabolites and used to establish their diagnostic potential. Logistic regression analysis was used to establish the diagnostic score.We show that NAFLD-HCC is characterised by a complete rearrangement of the serum lipidome, which distinguishes NAFLD-HCC from non-cancerous individuals and other HCC patients. We used machine learning to build a diagnostic model for NAFLD-HCC. We quantified predictive metabolites and developed the NAFLD-HCC Diagnostic Score (NHDS), presenting superior diagnostic potential compared to alpha-fetoprotein (AFP). Patients' metabolic landscapes show a progressive depletion in unsaturated fatty acids and acylcarnitines during transformation. Upregulation of fatty acid transporters in NAFLD-HCC tumours contribute to fatty acid depletion in the serum.NAFLD-HCC patients can be efficiently distinguished by serum metabolic alterations from the healthy population and from HCC patients related to other aetiologies (alcohol and viral hepatitis). Our model can be used for non-invasive surveillance of individuals with metabolic syndrome(s), allowing for early detection of NAFLD-HCC. Therefore, serum metabolomics may provide valuable insight to monitor patients at risk, including morbidly obese, diabetics, and NAFLD patients.The funding sources for this study had no role in study design, data collection, data analyses, interpretation or writing of the report as it is presented herein.