Harnessing artificial intelligence and machine learning for next-generation bioremediation

This review explores recent trends in microbial and enzyme-based bioremediation and emphasizes the integration of artificial intelligence (AI) and machine learning (ML) to enhance contaminant removal in water and wastewater ecosystems. Diverse microorganisms, including bacteria, fungi, microalgae, and engineered consortia, effectively degrade organic compounds, heavy metals, dyes, hydrocarbons, and polycyclic aromatic hydrocarbons (PAHs). AI/ML approaches have been applied for predictive modelling, real-time monitoring, and process optimization, improving operational efficiency, treatment stability, and cost-effectiveness. Non-combinable strategies such as enzyme immobilization, engineered bioreactors, and co-cultivation, alongside chemical pretreatments and phytoremediation, further enhance degradation efficiency. A total of 180 studies from 2018 to 2025 were reviewed, showing significant improvements in water quality parameters (BOD, COD, TDS, nitrates, phosphates, Mg, Ca, Zn) and high removal efficiencies for hydrocarbons, dyes, and pharmaceuticals. The literature underscores the potential of AI/ML-enabled, eco-friendly, and cost-efficient bioremediation solutions, while highlighting the need for large-scale validation, synergistic method optimization, and field-level implementation to tackle emerging water contaminants effectively.