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Shotgun metagenomic sequencing of the collective genomic information carried across microbial communities is emerging as a powerful approach for monitoring antibiotic resistance in environmental matrices. Metagenomics is advantageous in that known and putative antibiotic resistance genes (ARGs) (i.e., the resistome) can be screened simultaneously without a priori selection of targets. Additionally, as new ARGs are discovered and catalogued, stored sequencing data can be reanalyzed to assess the prevalence of emerging genes or pathogens. However, best practices for metagenomic data generation and processing are needed to support comparability across space and time. To support reproducible downstream analysis, guidance is first needed with respect to sampling design, sample preservation and storage, DNA extraction, library preparation, sequencing depth, and experimental controls. Here we conducted a systematic review to assess current practices for the application of metagenomics for AR profiling of wastewater, recycled water, and surface water and to offer recommendations to support comparability in the collection, production, and analysis of resulting data. Based on integrated analysis of findings and data reported across 95 articles identified, a field to benchtop metagenomic workflow is discussed for optimizing the representativeness and comparability of generated data. Through the reanalysis of 1474 publicly-available metagenomes, appropriate sequencing depths per environment and uniform normalization strategies are provided. Further, there is opportunity to harness the quantitative capacity of metagenomics more overtly through inclusion of sequencing controls. The recommendations will amplify the overall value of the metagenomic data generated to support within and between study comparisons, now and in the future. [ABSTRACT FROM AUTHOR]