Metagenomics and Multi-Omics Approaches in Petroleum Hydrocarbon Bioremediation: A Narrative Synthesis of Microbial Ecology, Predictive Modelling and Emerging Engineering Strategies

Petroleum hydrocarbons pose a major global environmental threat to both land and marine ecosystems. Cleaning up these pollutants is expensive and often environmentally damaging through traditional methods, while microbial bioremediation offers a more sustainable alternative. Recent advances in tools like metagenomics and multi-omics combining genomics, metatranscriptomics, metaproteomics, and metabolomics have transformed research on hydrocarbon-degrading microbes. This study explores how approaches such as metagenomics, multi-omics, systems biology, and AI can aid bioremediation, from identifying microbial communities to designing targeted cleanup systems. It uncovered extraordinary microbial diversity in contaminated sites. Integrating multi-omics revealed complex metabolic networks driving hydrocarbon breakdown. Machine learning can now predict bioremediation success with accuracy. Enhanced pollutant removal has been achieved through engineered strategies like synthetic microbial consortia, genome-scale metabolic models, and CRISPR-based strain modification, increasing efficiency 2-4 times over traditional methods. Prospects include innovative concepts such as multi-omics combined with digital twins, real-time AI monitoring, automated bioaugmentation, and synthetic ecology. The Predictive and Engineering-Driven Multi-Omics Bioremediation Framework (PEMBF) represents a fundamental shift by merging environmental monitoring, systems biology, machine learning, and synthetic microbial ecosystems into site-specific solutions. Nonetheless, significant challenges remain: understanding keystone taxa in complex microbiomes, translating lab results to field conditions, integrating spatial and temporal omics data, regulatory uncertainties surrounding engineered microbes, and scaling from pilot to industrial levels.