The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities
Open accessAbstract
Digital twinning is one of the top ten technology trends in the last couple of years, due to its high applicability in the industrial sector. The integration of big data analytics and artificial intelligence/machine learning (AI-ML) techniques with digital twinning, further enriches its significance and research potential with new opportunities and unique challenges. To date, a number of scientific models have been designed and implemented related to this evolving topic. However, there is no systematic review of digital twinning, particularly focusing on the role of AI-ML and big data, to guide the academia and industry towards future developments. Therefore, this article emphasizes the role of big data and AI-ML in the creation of digital twins (DTs) or DT-based systems for various industrial applications, by highlighting the current state-of-the-art deployments. We performed a systematic review on top of multidisciplinary electronic bibliographic databases, in addition to existing patents in the field. Also, we identified development-tools that can facilitate various levels of the digital twinning. Further, we designed a big data driven and AI-enriched reference architecture that leads developers to a complete DT-enabled system. Finally, we highlighted the research potential of AI-ML for digital twinning by unveiling challenges and current opportunities.
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High costs of implementation due to the increased amount of sensors and computational resources needed are a challenge for digital twin implementation.
“High costs of implementation due to the increased amount of sensors and computational resources needed [10,18].”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [18]
The “Data Collection” section (page unavailable) says that collecting and sharing sensor data can incur considerable costs, sometimes exceeding the cost of the physical asset. The “Big Data Challenges” section describes substantial data-processing needs, but the copy does not specifically attribute implementation costs to an increased number of sensors or to computational resources. (LLM, conf=0.84)
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- M. MAZHAR RATHORE (Member, IEEE) received the master's degree in computer and com- munication security from the National University of Sciences and Technology, Pakistan, in 2012, and the Ph.D. degree in computer science and engineering from Kyungpook National University, South Korea, in 2018. He is currently working as a Postdoctoral Researcher with the College of Science and Engineering, Hamad Bin Khalifa Uni- versity, Qatar. His research interests include big data analytics, the Internet of Things, smart systems, network traffic analysis and monitoring, remote sensing, smart city, urban planning, intrusion detection, and information security and privacy. He is a professional member of ACM. He received the Best Project/Paper Award in the 2016 Qual- comm Innovation Award at Kyungpook National University, for his paper ''IoT-Based Smart City Development Using Big Data Analytical Approach.'' He was also a nominee for the Best Project Award in the 2015 IEEE Communications Society Student Competition, for his Project ''IoT-Based Smart City.'' He is serving frequently as a Reviewer for various IEEE, ACM, Springer, and Elsevier journals.
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A Conceptual Framework for Digital Twin in Healthcare: Evidence from a Systematic Meta-Review
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Saporiti, N., Cannas, V. G., Pozzi, R., & Rossi, T. (2023) · International Journal of Production Economics · doi:10.1016/j.ijpe.2023.108888 · Cited by 60 · see citing works
Chen, S., Bekar, E. T., Bokrantz, J., & Skoogh, A. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.07.006 · Cited by 79 · see citing works
Robotics in the Construction Sector: Trends, Advances, and Challenges
Liu, Y., A.H., A., Haron, N. A., N.A., B., & Wang, H. (2024) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-024-02104-4 · Cited by 60 · see citing works
Pang, Y., He, T., Liu, S., Zhu, X., & Lee, C. (2024) · Advanced Science · doi:10.1002/advs.202306574 · Cited by 60 · see citing works
When is a simulation a digital twin? A systematic literature review
Wooley, A., Silva, D. F., & Bitencourt, J. (2023) · Manufacturing Letters · doi:10.1016/j.mfglet.2023.08.014 · Cited by 61 · see citing works
15 years of Big Data: a systematic literature review
Tosi, D., Kokaj, R., & Roccetti, M. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00914-9 · Cited by 59 · see citing works
Digital-Twin-Based Fire Safety Management Framework for Smart Buildings
Almatared, M., Liu, H., Abudayyeh, O. Y., Hakimi, O., & Sulaiman, M. (2023) · Buildings · doi:10.3390/buildings14010004 · Cited by 63 · see citing works
Sun, Z., Jayasinghe, S., Sidiq, A., Shahrivar, F., Mahmoodian, M., & Setunge, S. (2024) · Sensors · doi:10.3390/s25010059 · Cited by 76 · see citing works
Edge intelligence-assisted animation design with large models: a survey
Zhu, J., Hu, C., Khezri, E., & Ghazali, M. M. M. (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-024-00601-3 · Cited by 59 · see citing works
Thwe, M. M., Stefanov, A. I., Rajkumar, V. S., & Pálenský, P. (2025) · IEEE Access · doi:10.1109/access.2025.3580055 · Cited by 57 · see citing works
B5GEMINI: AI-Driven Network Digital Twin
Mozó, A., Karamchandani, A., Gómez-Canaval, S., Sanz, M., Moreno, J. I., & Pastor, A. (2022) · Sensors · doi:10.3390/s22114106 · Cited by 53 · see citing works
Optimal Sensor Placement for Structural Health Monitoring: A Comprehensive Review
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Conde, J., Muñoz-Arcentales, A., Romero, M., Rojo, J., Salvachúa, J., Huecas, G., & Alonso, Á. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101723 · Cited by 56 · see citing works
Adeagbo, M., Wang, S.-M., & Ni, Y.-Q. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102450 · Cited by 51 · see citing works
Taloma, R. L., Cuomo, F., Comminiello, D., & Pisani, P. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11093-7 · Cited by 53 · see citing works
A survey on blockchain-enabled federated learning and its prospects with digital twin
Liu, K., Yan, Z., Liang, X., Kantola, R., & Hu, C. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.08.001 · Cited by 52 · see citing works
Bariah, L., & Debbah, M. A. (2024) · IEEE Wireless Communications · doi:10.1109/mwc.133.2200447 · Cited by 52 · see citing works
Sifat, M. M. H., Das, S. K., & Choudhury, S. M. (2023) · Electric Power Systems Research · doi:10.1016/j.epsr.2023.109958 · Cited by 54 · see citing works
Digital twins: Transforming the chemical process industry—A review
Pal, P. K., Hens, A., Behera, N., & Lahiri, S. K. (2025) · The Canadian Journal of Chemical Engineering · doi:10.1002/cjce.25611 · Cited by 58 · see citing works
Integrating Digital Twins and Machine Learning for Advanced Control in Green Hydrogen Production
Feng, Z., Luo, Y., Li, D., Pan, J., Tan, R. Q., & Chen, Y. (2025) · CHAIN · doi:10.23919/chain.2025.000003 · Cited by 51 · see citing works
Vehicle system dynamics in digital twin studies in rail and road domains
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Digital twin-based excavation trajectory generation of Uncrewed excavators for autonomous mining
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A review on digital twin application in photovoltaic energy systems: challenges and opportunities
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Ranawaka, A. S. R. A., Alahakoon, D., Sun, Y., & Hewapathirana, K. (2024) · Energies · doi:10.3390/en17215342 · Cited by 54 · see citing works
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Structural health monitoring of timber bridges – A review
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Agricultural digital twin for smart farming: A review
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Digital twins and their use in future power systems
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Economic impact of digital twins on renewable energy investments
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Data Integration for Digital Twins in Industrial Automation: A Systematic Literature Review
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Onile, A. E., Petlenkov, E., Levron, Y., & Belikov, J. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.03.018 · Cited by 46 · see citing works
Data-Driven Digital Twin Framework for Predictive Maintenance of Smart Manufacturing Systems
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Sharotry, A., Jimenez, J. A., Mediavilla, F. A. M., Wierschem, D. C., Koldenhoven, R. M., & Valles, D. (2022) · IEEE Access · doi:10.1109/access.2022.3145984 · Cited by 39 · see citing works
- Authors
- Rathore, M. M., Shah, S. A., Shukla, D., Bentafat, E., & Bakiras, S.
- Affiliations
- Hamad bin Khalifa University (QA)
- University of Tartu (EE)
- University of New Brunswick (CA)
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 32030-32052
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3132985895
- Cited by
- 616
- References
- 157
- Citations by year
- 2026: 114 · 2025: 185 · 2024: 145 · 2023: 94 · 2022: 67 · 2021: 11
- FWCI
- 7.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Keywords
- digital twin, artificial intelligence, machine learning, big data analytics, industrial applications, reference architecture, patents
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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