Applications of Artificial Intelligence in Microbial Bioremediation: Current Advances, Challenges and Future Prospects
DOI:
https://doi.org/10.18178/JAAI.2026.4.4.200-222Keywords:
artificial intelligence, microbial bioremediation, machine learning, deep learning, metagenomics, enzyme prediction, process optimization, explainable AI, environmental remediationAbstract
Artificial Intelligence (AI) is increasingly applied to microbial bioremediation to manage complex relationships among microorganisms, pollutants and environmental conditions. This critical narrative review examines peer-reviewed literature published from 1 January 2020 to the final search date of 21 September 2026 and evaluates AI across a pathway extending from microorganism and enzyme selection to pollutant-removal prediction, process optimization, experimental validation and real-world translation. Recent studies show that machine learning can identify microbial indicators, predict attenuation and removal of heavy metals and organic contaminants, optimize petroleum-, phenol- and PCB-degrading systems, and mine genomic or metagenomic data for candidate enzymes. However, the evidence base remains uneven because datasets are often heterogeneous, models may overfit, external validation is limited, and computational associations do not necessarily establish biological causation. The review therefore distinguishes computational prediction from biochemical, whole-cell, consortium, realistic-matrix and field validation. It argues that the most useful future pathway is an iterative prediction-validation-optimization framework in which AI prioritizes biological candidates and operating conditions, experiments confirm or reject predictions, and monitoring or field observations refine models. AI should consequently be viewed as a decision-support layer that strengthens, rather than replaces, microbiological understanding and experimental validation.
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