Unraveling the Microbiome–Environmental Change Nexus to Contribute to a More Sustainable World: A Comprehensive Review of Artificial Intelligence Approaches

Maria Inês Barbosa, Gabriel Silva, Pedro Ribeiro, Eduarda Vieira, André Perrotta, Patrícia Moreira and Pedro Miguel Rodrigues ()
Additional contact information
Maria Inês Barbosa: CBQF—Centro de Biotecnologia e Química Fina, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
Gabriel Silva: CBQF—Centro de Biotecnologia e Química Fina, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
Pedro Ribeiro: CBQF—Centro de Biotecnologia e Química Fina, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
Eduarda Vieira: CITAR—Centro de Investigação em Ciência e Tecnologia das Artes, Escola das Artes, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
André Perrotta: Centre for Informatics and Systems of the University of Coimbra (CISUC), 3030-290 Coimbra, Portugal
Patrícia Moreira: CITAR—Centro de Investigação em Ciência e Tecnologia das Artes, Escola das Artes, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
Pedro Miguel Rodrigues: CBQF—Centro de Biotecnologia e Química Fina, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal

Sustainability, 2025, vol. 17, issue 16, 1-32

Abstract: This review aims to explore the literature to assess the potential of artificial intelligence (AI) in environmental monitoring for predicting microbiome dynamics. Recognizing the significance of comprehending microorganism diversity, composition, and ecologically sustainable impact, the review emphasizes the importance of studying how microbiomes respond to environmental changes to better grasp ecosystem dynamics. This bibliographic search examines how AI (Machine Learning and Deep Learning) approaches are employed to predict changes in microbial diversity and community composition in response to environmental and climate variables, as well as how shifts in the microbiome can, in turn, influence the environment. Our research identified a final sample of 50 papers that highlighted a prevailing concern for aquatic and terrestrial environments, particularly regarding soil health, productivity, and water contamination, and the use of specific microbial markers for detection rather than shotgun metagenomics. The integration of AI in environmental microbiome monitoring directly supports key sustainability goals through optimized resource management, enhanced bioremediation approaches, and early detection of ecosystem disturbances. This study investigates the challenges associated with interpreting the outputs of these algorithms and emphasizes the need for a deeper understanding of microbial physiology and ecological contexts. The study highlights the advantages and disadvantages of different AI methods for predicting environmental microbiomes through a critical review of relevant research publications. Furthermore, it outlines future directions, including exploring uncharted territories and enhancing model interpretability.

Keywords: microbiome; machine learning; deep learning; environment; forecasting; sustainability goals (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/16/7209/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/16/7209/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:16:p:7209-:d:1720990

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().