Abstract: The prediction of liver fibrosis stages in Hepatitis C virus (HCV) and Hepatitis B virus (HBV) is an important issue. The gold criterion for liver fibrosis stages evaluation is the liver biopsy but with a lot of drawbacks. So, it became necessary to use alternative methods to assess the degree of liver fibrosis. Many machine learning techniques were used as non-invasive alternative methods for doing the liver fibrosis prediction task to avoid the disadvantages of the liver biopsy. This study surveys many machine learning techniques that were applied for differentiation between the stages of hepatic fibrosis and liver fibrosis prediction on different medical HBV and HCV datasets using different blood tests and clinical parameters with applying several feature selection techniques. Also, the results and performance of classifier models are reviewed with comparison to non-invasive methods, which used for liver fibrosis prediction, such as APRI score and FIB-4 index score.