Biofiltration, a sustainable water treatment technology relying on microbial processes to remove contaminants, offers a promising approach to achieving the United Nations Sustainable Goal 6 of universal access to clean water and sanitation by 2030. However, a key barrier to optimising biofiltration is the incomplete understanding of the biological mechanisms governing its performance. Despite numerous studies examining how engineering decisions impact biofilter performance and the associated microbiome, the significant influence of geographical location on microbial communities raises the question of whether these findings are universally applicable or location-specific. To address this, we conducted a meta-analysis of 15 biofilter microbiomes using 16S rRNA high-throughput sequencing (HTS) data, mainly originating from rapid gravity and/or granular activated carbon (GAC) filters. Despite different types and scales, results highlight geographical location as the major contributor to microbiome dissimilarity in biofilter samples (Top and Bottom) (R2∼ 0.5; p-value<0.001). The same was observed for influent waters (PERMANOVA R2= 0.76; p-value<0.001), indicating location-specific microbiomes as opposed to differences driven by different biofilter operating parameters. Irrespective of location, the higher percentage of the microbiome was assembled through deterministic processes (∼55 %) compared to stochastic processes (∼45 %). Finally, our findings suggest that the depth stratification of biofilter microbiomes may be associated with the enrichment of taxa capable of metabolising more complex organic carbon in deeper filter layers (10 enriched pathways in biofilter Bottom layers compared to 3 at the Top). These insights provide a broader understanding of biofiltration microbiomes and offer possible research avenues for targeted and effective biofilter design strategies.