Both human and environmental health are seriously jeopardised by the rise of antimicrobial resistance (AMR).  The monitoring of antimicrobial resistance (AMR) in populations has become increasingly dependent on wastewater-based surveillance.  One promising comprehensive high-throughput approach to identifying and measuring resistance genes is metagenomics, which entails sequencing and analysing genetic material from environmental samples.  Nevertheless, its efficacy is constrained by methodological obstacles such inconsistent bioinformatics, low-abundance gene detection, and environmental heterogeneity.  Findings from important research on AMR monitoring using metagenomics in wastewater are synthesised in this review.  It emphasises new developments such as coupled qPCR-metagenomic methods for higher sensitivity and specificity, better bioinformatics pipelines, and synthetic DNA standards.  Machine learning for pattern identification and worldwide surveillance networks for coordinated monitoring are two options proposed in the study to fill up the gaps in standardisation and data interpretation.  In order to make metagenomic AMR monitoring in wastewater settings more accurate and scalable, it is essential to establish uniform procedures and to increase the worldwide surveillance infrastructure.