Background: Oil spills are a longstanding risk to the marine and coastal ecosystem, thus requiring efficient bioremediation processes. Microbial genomics and functional metagenomics are robust tools for the study of hydrocarbon-degrading communities, their catabolic pathways and applications in bioremediation. This systematic review of evidence from multiple oil-impacted ecosystems describes microbial response, functions and degradation mechanisms. Methods: A bibliographical search was conducted following the PRISMA recommendations in databases such as Cochrane, Web of Science, Scopus, ResearchGate and Google Scholar. Fourteen studies were included from screened records through duplicate removal, title/abstract screening and full text extraction and presented via thematic synthesis. Findings: Oil exposure constantly caused marked microbial re-structuring, where Proteobacteria were the most abundant group, and Archaea, as well Eukaryota constituted minor fractions. An initial signature of rich oil exposure was the dominance by particular taxa, giving way to increased diversity during succession. Degradation of hydrocarbons proceeded along oxygen and substrate dependent aerobic and anaerobic pathways, with selective reduction of short and mid-chain alkanes while long chain alkanes and polycyclic aromatic hydrocarbons were persistent. Pathway abundance tripled for functional genes among hydrocarbon degradation, nitrogen metabolism, stress response and xenobiotic processing. Active expression and high degradation efficiency were verified through metatranscriptomic and laboratory analyses. Oil contamination also linked the processing of hydrocarbons to nitrogen turnover and impacted bioremediation efforts. Conclusion: Oil spills mediate predictable taxonomic and functional changes, increase the metabolic potential of microbial communities, and demonstrate microbial communities evolved capacity for bioremediation in varied environments. Multi-omics approach coupled with field trials should be integrated in future studies to optimize interventions.