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Abstract Background Microbial bioremediation has emerged as a viable approach for ecosystem restoration and sustainable resource management. This approach offers a natural solution, that is cost-effective and environmentally sound alternative compared to conventional remediation methods. Main body This review synthesizes how microbial bioremediation approaches are being optimised in real time by use of AI and ML. AI models can significantly improve the effectiveness and scalability of the bioremediation by modelling degradation kinetics of microbes, predicting microbial interactions, determining the optimal environmental settings for microbial activity. Integration of multi-omics technologies such as transcriptomics, metabolomics and metagenomics have further enhanced the field allowing rational design of field-deployable microbes that are more robust with improved efficacy. Moreover, integration of nanotechnology has also significantly extended the scope of microbial bioremediation. Regardless of these advances, there remains significant challenges. Regulatory constraints, ecological uncertainties and requirement for improved data integration across omics platforms continues to be a barrier in field-scale applications. Furthermore, thorough risk assessment and policy support are necessary for safe deployment of genetically altered microbes for the translation of lab-scale innovations to real-world settings. This review thus aims to synthesise recent development in microbial ecology, systems biology, synthetic biology, and computational modelling within the context of microbial bioremediation. It investigates the application of AI-enhanced optimisation in bioremediation, omics-informed consortia design, and integrated One Health applications across soil, water, and air domains. Conclusion Bioremediation is transitioning from ad hoc interventions to predictive, monitored, and risk-aware programs. Integrating omics, synthetic biology, AI/ML, and smart monitoring within a One Health perspective can make deployments of microbes more reliable and scalable, if governance, safety, and data standards advance in parallel.
Pegu et al. (Fri,) studied this question.