Authors : Rabeta Sharmin

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/y7y84dkr

Scribd : https://tinyurl.com/mrybnp6b

DOI : https://doi.org/10.38124/ijisrt/26jul1411

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapid advancement of Artificial Intelligence (AI) and digital transformation technologies is fundamentally reshaping sustainable engineering systems across manufacturing, supply chain management, energy, infrastructure, and industrial operations. Modern engineering systems increasingly integrate intelligent technologies, including machine learning, digital twins, blockchain, cloud computing, and data analytics, to improve operational efficiency, resource utilization, decisionmaking, and environmental sustainability. Simultaneously, organizational leadership, circular economy principles, and effective project management have emerged as critical enablers for the successful adoption of digital transformation initiatives. This review presents a comprehensive overview of recent developments in AI-driven digital transformation for sustainable engineering systems by synthesizing research on digital leadership, circular economy, blockchain-enabled supply chains, machine learning, digital twin technologies, and intelligent engineering applications. The review first examines the role of leadership and organizational transformation in promoting sustainability and digital innovation. It then discusses the contributions of machine learning algorithms, including Random Forest and Gradient Boosting, toward intelligent decision support and predictive analytics. Furthermore, recent advances in digital twin technology and AI-assisted engineering applications are reviewed using reservoir characterization as an illustrative case study of intelligent engineering systems. Finally, current research challenges, including data integration, scalability, sustainability, interoperability, cybersecurity, and organizational readiness, are analyzed, followed by future research directions emphasizing explainable AI, human-centered digital transformation, and integrated intelligent engineering ecosystems. By consolidating diverse research areas into a unified framework, this review highlights how AI-enabled digital transformation can support resilient, efficient, and sustainable engineering systems while providing guidance for future interdisciplinary research and industrial implementation.

Keywords : Artificial Intelligence, Digital Transformation, Sustainable Engineering Systems, Machine Learning, Digital Twin, Leadership, Circular Economy, Blockchain, Industry 4.0.

References :

1. M. R. Islam, "System Dynamics of Leadership Influence in Sustainable Supply Chains," World Journal of Advanced Engineering Technology and Sciences, vol. 17, no. 03, pp. 509–514, 2025, doi: 10.30574/wjaets.2025.17.3.1584.
2. M. R. Islam, "Digital Leadership and Circular Economy Performance in Sustainable Supply Chains," World Journal of Advanced Engineering Technology and Sciences, vol. 17, no. 03, pp. 503–508, 2025, doi: 10.30574/wjaets.2025.17.3.1583.
3. M. R. Islam, "Circular economy leadership for sustainable industrial transformation: A holistic framework for resilient and resource-efficient growth," World Journal of Advanced Engineering Technology and Sciences, vol. 17, no. 03, pp. 253–262, 2025, doi: 10.30574/wjaets.2025.17.3.1540.
4. Y. A. Bipasha, M. R. Islam, and M. F. Ahmed, "A blockchain and machine learning framework for secure and transparent digital supply chain management," International Journal of Innovative Science and Research Technology, vol. 11, no. 3, pp. 945–952, Mar. 2026, doi: 10.38124/IJISRT/26MAR767.
5. M. R. Islam and A. Halim, "Developing a challenge-driven project management framework for sustainable development: An MCDM-based evaluation and prioritization approach," International Journal of Science and Research Archive, vol. 18, no. 01, pp. 177–187, 2026, doi: 10.30574/ijsra.2026.18.1.0027.
6. M. B. Uddin, M. R. Islam, M. N. Uddin, and A. Halim, "Next-generation plastic recycling: Breakthrough developments and the path toward a circular economy," World Journal of Advanced Engineering Technology and Sciences, vol. 16, no. 01, pp. 513–527, 2025, doi: 10.30574/wjaets.2025.16.1.1237.
7. R. A. Elbarouni, "A review of AI-driven seismic interpretation, digital twin technology and intelligent reservoir characterization for hydrocarbon exploration," World Journal of Advanced Engineering Technology and Sciences, vol. 09, no. 01, pp. 513–519, 2023, doi: 10.30574/wjaets.2023.9.1.0147.
8. R. A. Elbarouni, "AI-driven digital twin framework for real-time seismic reservoir monitoring and predictive hydrocarbon production optimization," World Journal of Advanced Engineering Technology and Sciences, vol. 11, no. 02, pp. 712–720, 2024, doi: 10.30574/wjaets.2024.11.2.0122.
9. R. A. Elbarouni, "Advanced 3D seismic interpretation techniques for accurate subsurface structural mapping and reservoir characterization," International Journal of Science and Research Archive, vol. 18, no. 03, pp. 992–1002, 2026, doi: 10.30574/ijsra.2026.18.3.0539.
10. R. A. Elbarouni, "Seismic attribute-based fault detection and structural mapping in 3D seismic data for hydrocarbon exploration," World Journal of Advanced Engineering Technology and Sciences, vol. 18, no. 03, pp. 320–329, 2026, doi: 10.30574/wjaets.2026.18.3.0166.
11. R. A. Elbarouni, "Integrated seismic and petrophysical analysis for improved hydrocarbon reservoir characterization," World Journal of Advanced Engineering Technology and Sciences, vol. 20, no. 01, pp. 196–207, 2026, doi: 10.30574/wjaets.2026.20.1.0364.
12. R. A. Elbarouni, "Machine learning-based seismic attribute analysis for porosity prediction and lithofacies classification in clastic hydrocarbon reservoirs," World Journal of Advanced Engineering Technology and Sciences, vol. 20, no. 01, pp. 231–240, 2026, doi: 10.30574/wjaets.2026.20.1.0366.
13. R. A. Elbarouni, "Uncertainty-aware reservoir characterization using probabilistic seismic–petrophysical integration," World Journal of Advanced Engineering Technology and Sciences, vol. 20, no. 01, pp. 219–230, 2026, doi: 10.30574/wjaets.2026.20.1.0365.
14. L. Breiman, "Random forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
15. J. H. Friedman, "Greedy function approximation: A gradient boosting machine," Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001.