Harnessing AI and Machine Learning for Sustainable Development: Applications, Challenges, and Future Trends
K. ARORA, K. JOSHI, ROHINI, UMANG, R.P. JAYASWAL, S.Y. BUKHARI
Abstract: We are just beginning to understand how transformative technologies such as artificially intelligent (AI) and machine learning (ML) can potentially transform the world of sustainability in a world where environmental issues are common and sustainable development is strongly prioritized. The efficiency, flexibility, and responsiveness of existing decay detection and air quality or energy monitoring strategies are undoubtedly rudimentary. They are especially skilled in predictive analytics, real-time robotics, and intelligent-centric decision-making; when utilized in the right manner, they totally revolutionize any sustainability plan towards the accomplishment of those goals on a global scale. Therefore, the article presents a brief overview of all the applications of AI and ML in sustainable practice, from precision farming, waste reduction, climate simulation, smart energy grids, and renewable energy forecasts. Every one of these uses takes into account how these newer technologies can enhance their operations, conserve resources, and reduce their environmental footprint. These technologies also create some thought-provoking questions about their own detriments, such as bad data quality, expensive processing, ethical dilemmas, and strict regulations. Among the outcomes of the more recent inventive creations, pragmatic uses, and academic research on the topic are expressions of the critical significance, utmost care, and responsible behavior of artificial intelligence and machine learning toward a democratic and sustainable future. The action graciously defies the technological challenges; the technologies must find new ways of advancing justice and environmental awareness.
Keywords
Artificial Intelligence, Machine Learning, Smart Energy Systems, Sustainable Development, Environmental Monitoring
Published online 5/10/2026, 10 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: K. ARORA, K. JOSHI, ROHINI, UMANG, R.P. JAYASWAL, S.Y. BUKHARI, Harnessing AI and Machine Learning for Sustainable Development: Applications, Challenges, and Future Trends, Materials Research Proceedings, Vol. 66, pp 224-233, 2026
DOI: https://doi.org/10.21741/9781644904152-20
The article was published as article 20 of the book Advanced Materials and Sustainable Energy Technologies
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] Van Wynsberghe, A. (2021). Sustainable AI: AI for sustainability and the sustainability of AI. AI and Ethics, 1(3), 213-218. https://doi.org/10.1007/s43681-021-00043-6
[2] Kar, A. K., Choudhary, S. K., & Singh, V. K. (2022). How can artificial intelligence impact sustainability: A systematic literature review. Journal of Cleaner Production, 376, 134120. https://doi.org/10.1016/j.jclepro.2022.134120
[3] Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International journal of information management, 53, 102104. https://doi.org/10.1016/j.ijinfomgt.2020.102104
[4] Kar, A. K., Choudhary, S. K., & Singh, V. K. (2022). How can artificial intelligence impact sustainability: A systematic literature review. Journal of Cleaner Production, 376, 134120. https://doi.org/10.1016/j.jclepro.2022.134120
[5] Fan, Z., Yan, Z., & Wen, S. (2023). Deep learning and artificial intelligence in sustainability: a review of SDGs, renewable energy, and environmental health. Sustainability, 15(18), 13493. https://doi.org/10.3390/su151813493
[6] Srivastava, S.S., Satyanarayana, G.S., Dhasmana, A., Rawat, V., Rana, A.S. and Bisht, Y.S., 2025. Role of Embedded Systems in Smart Energy Management: Challenges, Innovations, and Future Trends. Solar Energy and Sustainable Development Journal, 14(STR2E), pp.27-50. https://doi.org/10.51646/jsesd.v14iSTR2E.797
[7] Strubell, E., Ganesh, A., & McCallum, A. (2020, April). Energy and policy considerations for modern deep learning research. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 09, pp. 13693-13696) https://doi.org/10.1609/aaai.v34i09.7123
[8] Begum, A., Naim, A., & Sabahath, A. (2024). The Impact of AI on Sustainability. In Harnessing High-Performance Computing and AI for Environmental Sustainability (pp. 99-113). IGI Global. https://doi.org/10.4018/979-8-3693-1794-5.ch005
[9] Asha, P., Mannepalli, K., Khilar, R., Subbulakshmi, N., Dhanalakshmi, R., Tripathi, V., … & Sudhakar, M. (2022). Role of machine learning in attaining environmental sustainability. Energy Reports, 8, 863-871. https://doi.org/10.1016/j.egyr.2022.09.206
[10] Gundeti, R., Vuppala, K., & Kasireddy, V. (2024). The future of AI and environmental sustainability: Challenges and opportunities. Exploring Ethical Dimensions of Environmental Sustainability and Use of AI, 346-371. https://doi.org/10.4018/979-8-3693-0892-9.ch017
[11] Saravanan, S., Khare, R., Umamaheswari, K., Khare, S., Gowda, B. K., & Boopathi, S. (2024). AI and ML Adaptive Smart-Grid Energy Management Systems: Exploring Advanced Innovations. In Principles and Applications in Speed Sensing and Energy Harvesting for Smart Roads (pp. 166-196). IGI Global. https://doi.org/10.4018/978-1-6684-9214-7.ch006
[12] Khan, S. U., Khan, N., Ullah, F. U. M., Kim, M. J., Lee, M. Y., & Baik, S. W. (2023). Towards intelligent building energy management: AI-based framework for power consumption and generation forecasting. Energy and buildings, 279, 112705. https://doi.org/10.1016/j.enbuild.2022.112705
[13] Zafar, M. H., Khan, N. M., Mansoor, M., Mirza, A. F., Moosavi, S. K. R., & Sanfilippo, F. (2022). Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems. Energy Conversion and Management, 258, 115564. https://doi.org/10.1016/j.enconman.2022.115564
[14] Yang, Y., Bremner, S., Menictas, C., & Kay, M. (2022). Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review. Renewable and Sustainable Energy Reviews, 167, 112671. https://doi.org/10.1016/j.rser.2022.112671
[15] Gupta, P. K., Shree, V., Hiremath, L., & Rajendran, S. (2019). The use of modern technology in smart waste management and recycling: artificial intelligence and machine learning. Recent advances in computational intelligence, 173-188. https://doi.org/10.1007/978-3-030-12500-4_11
[16] Manoharan, R. Waste to Energy Conversion and Predictive maintenance in Circular Economics Through AI. IJSAT-International Journal on Science and Technology, 16(1).
[17] Taghikhah, F., Erfani, E., Bakhshayeshi, I., Tayari, S., Karatopouzis, A., & Hanna, B. (2022). Artificial intelligence and sustainability: solutions to social and environmental challenges. In Artificial intelligence and data science in environmental sensing (pp. 93-108). Academic Press. https://doi.org/10.1016/B978-0-323-90508-4.00006-X
[18] Vorozheykina, T. M. (2022). Challenges and prospects of decarbonization of the economy in the age of AI. Frontiers in Environmental Science, 10, 952821. https://doi.org/10.3389/fenvs.2022.952821
