Microbial omics has progressed from isolated genomic analyses into a comprehensive, integrated multi-omics framework, profoundly advancing our understanding of microbial complexity and functionality. This mini-review systematically outlines the core technologies within microbial omics—including genomics, transcriptomics, proteomics, and metabolomics—by introducing their fundamental principles, common experimental workflows, and state-of-the-art bioinformatic strategies. We particularly highlight the emergence of single-cell microbial omics as a transformative methodology that resolves molecular and functional heterogeneity within communities, enabling the identification of rare taxa, strain-level microdiversity, and specialized functional roles that are obscured in bulk analyses. Furthermore, we discuss how artificial intelligence (AI)-driven tools are revolutionizing the interpretation of high-dimensional omics data, uncovering latent biological patterns, improving predictive modeling of microbial behavior, and facilitating the translation of microbiome insights into clinical and environmental applications. The review concludes by comparing the strengths, limitations, and optimal use cases of each omics layer and single-cell approach while also addressing ongoing technical challenges and future directions in the field.