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The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in >omic> data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent >omics> approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges. ; SH, DM, MP, RM-T, SW, IT, HS, JE, SK, GA, and CC acknowledge the support of ERA-net gas co-fund for funding (Project name: RumenPredict). SH, HM and CC acknowledge support from BBSRC (BBL/L026716/1 and BBL/L026716/2) and a British Council Newton Institutional Links funding (Grant 172629373). IM acknowledges funding from the European Research Council under the European Union's Horizon 2020 research and innovation program (Grant 640384). JE acknowledges funding from an EU H2020 Marie Curie Fellowship (706899). CC, AK-S, and EH were supported by the Biotechnology and Biological Sciences Research Council (Grants BBS/OS/GC/000011B and BBS/E/W/0012843D). CN and OM acknowledge the support of the British Council Newton Institutional Links funding (Grant 216425215). SRUC receives financial support from the Scottish Government's Rural and Environment Science and Analytical Services Division (RESAS). RD and RR acknowledge financial support from the Biotechnology and Biological Sciences Research Council (BBSRC BB/N01720X/1). DY-R and AB acknowledge funding from MINECO, Spain (Grant AGL2017-86938-R). GS acknowledges funding from the U.S. Department of Agriculture National Institute of Food and Agriculture foundational (Grant 2015-67015-23246). EP acknowledges funding from CNPq (Grant 401590/2014-3). All authors are also members of the Global Research Alliance Rumen Microbial Genomics network. ; Peer Reviewed