Gut microbiome dysbiosis has been known to be associated with all stages of non-alcoholic fatty liver disease (NAFLD), but questions remain about microbial profiles in progression and homogeneity across NAFLD stages. We performed a meta-analysis of three publicly shotgun datasets and built predictive models to determine diagnostic capacity. Here, we found consistently microbiome shifts across NAFLD stages, of which co-occurrence patterns and core sets of new biomarkers significantly correlated with NAFLD progression were identified. Machine learning models that are able to distinguish patients with any NAFLD stage from healthy controls remained predictive when applied to patients with other NAFLD stages, suggesting the homogeneity across stages once again. Focusing on species and metabolic pathways specifically associated with progressive stages, we found that increased toxic metabolites and decreased protection of butyrate and choline contributed to advanced NAFLD. We further built models discriminating one stage from the others with an average of 0.86 of area under the curve. In conclusion, this meta-analysis firmly establishes generalizable microbiome dysbiosis and predictive taxonomic and functional signatures as a basis for future diagnostics across NAFLD stages.