Understanding the current weather conditions of different countries within the African continent, along with their agricultural and development status, is crucial for establishing the novel use of AI in environmental monitoring and climate adaptation. Africa, being the second-largest continent in the world, indicates the urgency to forecast and prioritise climate-related disasters accurately, which is possible using artificial intelligence (AI). This study investigates the future of environmental monitoring and climate change adaptation in the AI age. The study employed a desktop research method, utilising credible journals and publications from verifiable sources. The findings revealed that AI has been deployed globally in sustainable forest management, conservation, and environmental monitoring, with great potential in agriculture. The study recommends skill analysis to determine the climate-specific areas to prioritise, with a reporting timeline to measure progress. 

Artificial intelligence, climate adaptation, climate change, environmental monitoring, sustainable development

1. Albahri, A.S., Khaleel, Y.L., Habeeb, M.A., Ismael, R.D., Hameed, Q.A., Deveci, M., Homod, R.Z., Albahri, O.S., Alamoodi, A.H., & Alzubaidi, L. (2024). A systematic review of trustworthy artificial intelligence applications in natural disasters, Computers and Electrical Engineering, 118, Part B. DOI:10.1016/j.compeleceng.2024.109409.

2. American Chamber of Commerce Ghana AMCHAM (2025). Google Launches AI Community Center in Ghana With $37M Investment, Deepening U.S.–Africa Tech Collaboration. Available at https://amchamghana.org

3. Chisom, O.N., Biu, P.W., Umoh, A.A., Obaedo, B.O., Adegbite, A.O., & Abatan, A. (2024). Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet, World Journal of Advanced Research and Reviews, 21(01), 161–171

4. Devez, J. (2011). Challenges for Africa agriculture. The International Bank for Reconstruction and Development / The World Bank. World Bank General Services Department's Translation and Interpretation unit, pg. 3. eISBN: 978-0-8213-8515-9 DOI: 10.1596/978-0-8213-8481-7https://openknowledge.worldbank.org/

5. Dahake, R., Metre, K., Kale, N., Dange, B.J. & Mahankale, N. (2024). AI-Powered Environmental Monitoring and Conservation Strategies, International Journal of Engineering Trends and Technology, 72(8), 1-7. DOI:10.14445/22315381/IJETT-V72I8P101

6. De Perez, E. C., Berse, K. B., Depante, L. A. C., Easton-Calabria, E., Evidente, E. P. R., Ezike, T., Heinrich, D., Jack, C., Lagmay, A.M.F.A., Lendelvo, S., Marunye, J., Maxwell, D.G., Murshed, S.B., Orach, C.G., Pinto, M., Poole, L.B., Rathod, K., Shampa, Van Sant, C. (2022). Learning from the past in moving to the future: invest in communication and response to weather early warnings to reduce death and damage. Climate Risk Management, 38, 100461.

7. Degila, J., Tognisse, I. S., Honfoga, A.C., Houetohossou, S.C.A., Sodedji, F.A.K., Avakoudjo, H.G.G., Tahi, S. P. G., & Assogbadjo, A. E. (2023). A Survey on Digital Agriculture in Five West African Countries. Agriculture, 13(5), 1067. https://doi.org/10.3390/agriculture13051067

8. Foster, L., Szilagyi,K., Wairegi, A., Oguamanam, C., & de Beer, J. (2023). Smart farming and artificial intelligence in East Africa: Addressing indigeneity, plants, and gender, Smart Agricultural Technology, 3, DOI:10.1016/j.atech.2022.100132.

9. Gesami, B.K. & Nunoo, J. (2024). Artificial intelligence in marine ecosystem management: Addressing climate threats to Kenya's blue economy. Sec. Ocean Solutions, 11, DOI:10.3389/fmars.2024.1404104

10. IBM's Green Horizons uses IoT for clean air. (2015) https://www.smart-energy.com/regional- news/africa-middle-east/ibms-green-horizons-uses-iotfor-clean-air/

11. Keeffee, G.O. (2024). The Impact of AI on Agriculture in Kenya and Nigeria. The Borgen Project. https://borgenproject.org/impact-of-ai-on-agriculture/

12. Moro, O., Omojola, A.S., Fasona, M.J. (2023). A Review of Remote Sensing in Monitoring Oil and Gas Operations in Nigeria. Nigerian Journal of Geosciences and Environmental Research, 3(2), 47-60.

13. Matias, Y. (2022). Google Research enhances its AI growth in Africa. Available at https://blog.google/intl/en-africa

14. Mana, A.A., Allouhi, A., Hamrani, A., Rehman, S., Jamaoui, I., & Jayachandran, K. (2024). Sustainable AI-based production agriculture: Exploring AI applications and implications in agricultural practices, Smart Agricultural Technology, 7, 1-15, DOI:10.1016/j.atech.2024.100416.

15. Olawade, D. B., Wada, O. Z., Ige, A. O., Egbewole, B. I., Olojo, A., & Oladapo, B. I. (2024). Artificial intelligence in environmental monitoring: Advancements, challenges, and future directions. Hygiene and Environmental Health Advances, 12, 100114. https://doi.org/10.1016/j.heha.2024.100114

16. Oduoye MO, Cakwira H, Muhammad AI, Biamba C, Abubakar H, Osinowo GA, Vandi JJD, Akilimali A. The outlook of food security and food safety in Africa: Correspondence. Ann Med Surg (Lond). 85(4):1314-1315. DOI:10.1097/MS9.0000000000000336.

17. Omotayo, A.O., Ogunniyi, A.I., & Aremu, A.O. (2019). Data on food insufficiency status in South Africa: insight from the South Africa General Household Survey. Data Brief, 23, 1-6. https://doi.org/10.1016/j.dib.2019.103730

18. Obonyo, R. (2024). Making Artificial Intelligence safe in Africa. Stakeholders call for balanced AI regulations to harness its potential for societal good. United Nations Africa Renewal. Available at https://africarenewal.un.org/en/magazine/making-artificial-intelligence-safe-africa

19. Roman, V. (2025). Burn scar detection and its role in wildfire recovery. Available at https://developer.ibm.com/articles/awb-burn-scar-detection-wildfire-recovery/

20. Reichstein, M., Benson, V., Blunk, J., Creutzig, F., Fearnley, C. J., Han, B., Kornhuber, K., Rahaman, N., Schölkopf, B., Tárraga, J. M., Vinuesa, R., Dall, K., Denzler, J., Frank, D., Martini, G., Nganga, N., Maddix, D. C., & Weldemariam, K. (2025). Early warning of complex climate risk with integrated artificial intelligence. Nature Communications, 16(1), 1-13. DOI:10.1038/s41467-025-57640-w

21. Shaikh, R.A., Khan, S.K.S., Golhar, S.N., Nasim, M. (2024). AI for climate change: Enhancing predictive models for climate patterns and AI-driven environmental monitoring. Journal of Emerging Technologies and Innovative Research, 11(10), 517-520

22. Sharma, N.K. (2025). Harnessing Artificial Intelligence for Environmental Sustainability: Opportunities, Applications, Impacts and Challenges. International Journal of Scientific Research and Engineering Development, 8(3), 5536-5544

23. Taylor, R.R. & Munoriyarwa, A., The Climate Imperative: How AI Can Transform Africa's Future (2024). DOI:10.2139/ssrn.5233767

24. Tekie, B. (2024). Can COP29 ensure equitable climate finance for Africa's development?How COP29 could reshape climate finance to support Africa's development agenda and just energy transitions. Article UNU Institute for Natural Resources in Africa. Available at https://unu.edu/inra

25. Van Rees, C. B., Naslund, L., Hernandez-Abrams, D. D., McKay, S. K., Woodson, C. B., Rosemond, A., McFall, B., Altman, S., & Wenger, S. J. (2022). A strategic monitoring approach for learning to improve natural infrastructure. Science of The Total Environment, 832, 155078. DOI:10.1016/j.scitotenv.2022.155078

26. Wudil, A.H., Usman, M., Rosak-Szyrocka, J., Pilař, L., Boye, M. (2022). Reversing years for global food security: A review of the food security situation in sub-Saharan Africa (SSA). Int J Environ Res Public Health, 19(22), 1-22. DOI:10.3390/ijerph192214836.

27. Wang, T., Zuo, Y., Manda, T., Hwarari, D., & Yang, L. (2025). Harnessing artificial intelligence, machine learning and deep learning for sustainable forestry management and conservation: Transformative potential and future perspectives. Plants, 14(7), 998. DOI:10.3390/plants14070998

28. Xiang, X., & Meadows, M.E.(2025). Being proactive about anthropogenic environmental changes: Augmenting students' decision-making with artificial intelligence (AI) technology. Education Tech Research Dev. DOI:10.1007/s11423-025-10523-9