Artificial Intelligence Driven Bioinformatics for Sustainable Bioremediation: Integrating Computational Intelligence with Ecological Restoration

Abstract

Environmental pollution from heavy metals and untreated wastewater poses significant risks to ecosystems and human health, highlighting the urgent need for innovative remediation strategies. Bioremediation employs microorganisms to break down contaminants, presents a sustainable and economical solution. However, conventional techniques such as bioaugmentation and bio-stimulation face challenges due to inefficiencies and the absence of real-time monitoring. This narrative review consolidates the latest developments in AI-driven bioinformatics aimed at enhancing microbial bioremediation, with an emphasis on the degradation of heavy metals and wastewater pollutants. Advanced computational models such as Random Forest, Artificial Neural Networks, and Support Vector Machines demonstrate high predictive accuracy (R² > 0.99) in analysing microbial behaviour, pollutant dynamics, and optimizing processes. Bioinformatics tools such as AlphaFold2, I-TASSER, and metagenomic platforms such as QIIME and MG-RAST facilitate accurate identification of microbial communities, genes, and degradation pathways. AIpowered biosensors and advanced deep learning enable the continuous observation of enzymatic activity and the effectiveness of treatments. The combination of AI, metagenomics, and gene editing techniques, such as CRISPR, presents scalable approaches for achieving sustainable bioremediation. Present work emphasizes innovative tools, practical applications such as ANN-RF hybrid models, and prospective pathways, highlighting the significant impact of computational intelligence on ecological restoration.