Makieu, Peter2026-10-192026-09-25Makieu, P., Massaquoi, S. M., Momoh, J., Kamara, S., Howe, A. S., Hinneh, D. K., Kargbo, M., & Kabia, A. J. (2026). Artificial Intelligence-guided Engineered Nano-biochar for Carbon Management, Climate-smart Agriculture and Emerging Contaminant Remediation: A Critical Narrative Review. Asian Journal of Advances in Agricultural Research, 26(9), 83–101. https://doi.org/10.9734/ajaar/2026/v26i976010.9734/ajaar/2026/v26i9760https://doi.org/10.9734/ajaar/2026/v26i9760https://dspace.njala.edu.sl/handle/20.500.144402/211Biochar sits at the intersection of biomass valorisation, carbon management, soil restoration and contaminant control, while nano-engineering and artificial intelligence (AI) are increasingly proposed as means of tailoring its properties and accelerating design. Yet the phrase “AI-driven engineered nano-biochar” risks implying a level of technological integration and field validation that the evidence has not yet achieved. This critical narrative review evaluates that intersection by separating three evidence layers: established knowledge on bulk biochar, rapidly expanding evidence on engineered and nanoscale biochar, and emerging applications of machine learning to biochar production and performance prediction. Literature published from 1 January 2000 to 19 July 2026 was appraised for methodological quality, scale, mechanistic relevance, environmental realism and claim-to-evidence alignment. The evidence is strongest for the persistence of a fraction of pyrolytic carbon, context-dependent improvements in soil properties and crop performance, and enhanced adsorption after selected physical or chemical modifications. Machine-learning studies can predict biochar yield, composition and adsorption outcomes with high within-dataset accuracy, but their external validity is constrained by heterogeneous literature-derived datasets, sparse reporting of negative results, inconsistent material characterisation and limited prospective validation. Nanostructuring can increase accessible surface area, reactive sites and dispersibility, but the same attributes raise unresolved questions about particle transport, ecotoxicity, recovery, ageing and carbon permanence. Field evidence for nano-biochar remains comparatively scarce, although recent rice and salinity studies demonstrate agricultural promise under defined conditions. For emerging contaminants, most evidence remains batch-scale and equilibrium-centred, with limited treatment of realistic mixtures, dissolved organic matter, continuous flow, regeneration and spent-sorbent management. Carbon neutrality therefore cannot be inferred from adsorption capacity, crop response or carbon content alone; it requires life-cycle accounting, durable carbon measurement, energy and reagent inventories, counterfactual biomass fate, and monitoring of downstream risks. The most defensible near-term pathway is not autonomous AI discovery, but uncertainty-aware, multi-objective decision support coupled to standardised experiments, field trials and life-cycle assessment.Biochar sits at the intersection of biomass valorisation, carbon management, soil restoration and contaminant control, while nano-engineering and artificial intelligence (AI) are increasingly proposed as means of tailoring its properties and accelerating design. Yet the phrase “AI-driven engineered nano-biochar” risks implying a level of technological integration and field validation that the evidence has not yet achieved. This critical narrative review evaluates that intersection by separating three evidence layers: established knowledge on bulk biochar, rapidly expanding evidence on engineered and nanoscale biochar, and emerging applications of machine learning to biochar production and performance prediction. Literature published from 1 January 2000 to 19 July 2026 was appraised for methodological quality, scale, mechanistic relevance, environmental realism and claim-to-evidence alignment. The evidence is strongest for the persistence of a fraction of pyrolytic carbon, context-dependent improvements in soil properties and crop performance, and enhanced adsorption after selected physical or chemical modifications. Machine-learning studies can predict biochar yield, composition and adsorption outcomes with high within-dataset accuracy, but their external validity is constrained by heterogeneous literature-derived datasets, sparse reporting of negative results, inconsistent material characterisation and limited prospective validation. Nanostructuring can increase accessible surface area, reactive sites and dispersibility, but the same attributes raise unresolved questions about particle transport, ecotoxicity, recovery, ageing and carbon permanence. Field evidence for nano-biochar remains comparatively scarce, although recent rice and salinity studies demonstrate agricultural promise under defined conditions. For emerging contaminants, most evidence remains batch-scale and equilibrium-centred, with limited treatment of realistic mixtures, dissolved organic matter, continuous flow, regeneration and spent-sorbent management. Carbon neutrality therefore cannot be inferred from adsorption capacity, crop response or carbon content alone; it requires life-cycle accounting, durable carbon measurement, energy and reagent inventories, counterfactual biomass fate, and monitoring of downstream risks. The most defensible near-term pathway is not autonomous AI discovery, but uncertainty-aware, multi-objective decision support coupled to standardised experiments, field trials and life-cycle assessment.enCC BY 4.0https://creativecommons.org/licenses/by/4.0biocharArticle