Abstract Liver cancer is a major cause of cancer mortality worldwide. Screening individuals at high-risk, including those with cirrhosis and viral hepatitis, provides an avenue for improved survival, but current screening methods are inadequate. In this study, we used whole-genome cell-free DNA fragmentome analyses to evaluate 724 individuals from the US, EU, or Hong Kong with hepatocellular carcinoma (HCC) or who were at average or high-risk for HCC. Using a machine learning model that incorporated multi-feature fragmentome data, the sensitivity for detecting cancer was 88% in an average risk population at 98% specificity, and 85% among high-risk individuals at 80% specificity. We validated these results in an independent population. cfDNA fragmentation changes reflected genomic and chromatin changes in liver cancer, including from transcription factor binding sites. These findings provide a biological basis for changes in cfDNA fragmentation in patients with liver cancer and provide an accessible approach for non-invasive cancer detection.