Food security is increasingly at risk due to the expansion of the global population as well as concurrent threats to the ecosystem, including soil degradation and erosion, climate change, and biodiversity loss. To prevent further threats to the ecosystem, utilising plant and soil-associated microorganisms, biofertilisers offer a promising method to eliminate the application of agrochemical fertilisers. Biofertilisers are microbial substances such as bacteria, fungi, and algae that promote plant nutrition and growth by enhancing soil nutrient availability and mobility. Exploring biofertilisers, especially in conjunction with advanced multi-omics technologies such as genomics, transcriptomics, proteomics, metabolomics, and phenomics, represents a significant opportunity in agriculture. However, these technologies have not been effectively utilised, and there are limitations in the advancement of innovative and improved biofertiliser technologies. The application of multi-omics technologies in the study of biofertilisers can be a noteworthy step forward in improving the scientists’ comprehension of microbiome intricacies, allowing for in-depth exploration and characterization of the microbial community structure, functionality, and mechanism of action in plant microbiomes. The aforementioned discoveries will significantly enhance our understanding of soil microbial communities and their relationships with host plants, as well as their impact on plant nutrition and other plant growth-promoting characteristics. For example, metagenomic sequences have valuable insight into the potential functions of the rhizosphere community and assessments of interactions between microbes in plants, while metabolomics analysis is being used to diagnose plant diseases and their etiological agents. However, the application to microbiome science remains limited. Despite the recent progress in omics investigation on biofertilisers, it is just the tip of the iceberg of this emerging knowledge and the related techniques. A comprehensive understanding of these traits will be achieved once we gain further insights into their expression in natural environments through effective utilisation of complementary high-throughput techniques.