BACKGROUND: The impact of gut microbiome on hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) is unclear. We aimed to evaluate the potential correlation between gut microbiome and HBV-related HCC and introduced novel machine learning (ML) signatures based on gut microbe to predict the risk of HCC. MATERIALS AND METHODS: A total of 640 patients with chronic liver diseases or HCC were prospectively recruited between 2019 and 2022. Fecal samples were collected and subjected to 16S rRNA gene sequencing. Univariate and multivariate logistic regression was applied to identify risk characteristics. Several ML methods were employed to construct gut microbe-based models and the predictive performance was evaluated. RESULTS: A total of 571 patients were involved in the study, including 374 patients with HCC and 197 patients with chronic liver diseases. After the propensity score matching method, 147 pairs of participants were enrolled in the analysis. Bacteroidia and Bacteroidales were demonstrated to exert mediating effects between HBV and HCC, and the moderating effects varied across Bacilli, Lactobacillales, Erysipelotrichaceae, Actinomyces, and Roseburia. HBV, alpha-fetoprotein, alanine transaminase, triglyceride, and Child-Pugh were identified as independent risk factors for HCC occurrence. Seven ML-based HBV-gut microbe models were established to predict HCC, with AUCs ranging from 0.821 to 0.898 in the training set and 0.813-0.885 in the validation set. Furthermore, the merged clinical-HBV-gut microbe models exhibited a comparable performance to HBV-gut microbe models. CONCLUSIONS: Gut microbes are important factors between HBV and HCC through its potential mediating and moderating effects, which can be used as valuable biomarkers for the pathogenesis of HBV-related HCC.