AI-ENABLED DECISION SUPPORT SYSTEMS FOR SUSTAINABLE DEVELOPMENT: A COMPREHENSIVE REVIEW OF OPPORTUNITIES, APPLICATIONS, CHALLENGES, AND FUTURE RESEARCH DIRECTIONS

Authors

  • Madhuri Gupta Assistant Professor, Department of Computer Science, Mind Power University, Bhimtal
  • Aadarsh Joshi Assistant Professor, Computer Science and Engineering, Mind Power University, Bhimtal

Keywords:

Artificial Intelligence, Decision Support System, Sustainable Development, Machine Learning, Deep Learning, Explainable AI, Smart Cities, IoT, Optimization

Abstract

Artificial Intelligence (AI) has emerged as a transformative technology for enhancing decision-making across diverse sectors including healthcare, agriculture, transportation, manufacturing, environmental management, education, and governance. AI-enabled Decision Support Systems (AI-DSS) integrate machine learning, deep learning, optimization techniques, big data analytics, Internet of Things (IoT), and cloud computing to assist policymakers, industries, and organizations in making accurate, efficient, and sustainable decisions. Sustainable development requires balancing economic growth, environmental protection, and social equity while addressing complex global challenges such as climate change, resource depletion, food security, and public health. Traditional Decision Support Systems often fail to process massive real-time heterogeneous datasets and dynamic environmental conditions, whereas AI-enabled DSS provide predictive intelligence, adaptive learning, and automated reasoning capabilities.

This review comprehensively examines recent advancements in AI-enabled DSS for sustainable development by synthesizing literature published between 2020 and 2026. The review discusses enabling technologies, application domains, benefits, implementation challenges, ethical concerns, explainable AI, governance issues, and future research directions. It also identifies research gaps and proposes an integrated conceptual framework for next-generation sustainable AI decision systems.

References

I. Russell, S., &Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.).

II. United Nations. (2023). The Sustainable Development Goals Report.

III. OECD. (2024). AI Principles and Responsible AI.

IV. European Commission. (2024). Ethics Guidelines for Trustworthy AI.

V. Goodfellow, I., Bengio, Y., &Courville, A. Deep Learning.

VI. Sutton, R. S., &Barto, A. G. Reinforcement Learning: An Introduction.

VII. Lundberg, S. M., & Lee, S. I. (2017). A Unified Approach to Interpreting Model Predictions.

VIII. Ribeiro, M. T., Singh, S., &Guestrin, C. (2016). Why Should I Trust You? Explaining the Predictions of Any Classifier.

IX. Kusiak, A. (2024). Artificial Intelligence in Smart Manufacturing.

X. Recent review articles (2022–2026) on AI-enabled DSS, Explainable AI, Smart Cities, Sustainable AI, and AI for SDGs from journals such as Expert Systems with Applications, Decision Support Systems, Applied Soft Computing, Journal of Cleaner Production, Sustainable Cities and Society, and IEEE Access.

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Published

01-08-2026

How to Cite

Madhuri Gupta, & Aadarsh Joshi. (2026). AI-ENABLED DECISION SUPPORT SYSTEMS FOR SUSTAINABLE DEVELOPMENT: A COMPREHENSIVE REVIEW OF OPPORTUNITIES, APPLICATIONS, CHALLENGES, AND FUTURE RESEARCH DIRECTIONS. International Educational Journal of Science and Engineering, 9(08), 38–42. Retrieved from https://iejse.com/journals/index.php/iejse/article/view/418

Issue

Vol. 9 No. 08 (2026): SPECIAL ISSUE AUGUST - EMERGING TECHNOLOGIES FOR SUSTAINABLE DEVELOPMENT

Section

Articles

License

Copyright (c) 2026 International Educational Journal of Science and Engineering

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This work is licensed under a Creative Commons Attribution 4.0 International License.