article · IEEE Access
Combining blockchain and machine learning brings together decentralised, tamper-proof ledgers and data-driven artificial intelligence systems. This convergence can strengthen data privacy, raise analytical accuracy, and automate intricate procedures across digital networks. An evaluation of 700 academic manuscripts published between 2017 and 2022 maps the global landscape, key contributors, and evolving themes within this joint domain. Utilising specialised mapping tools, the review traces productivity patterns, institutional contributions, and collaborative networks. The results highlight several critical directions for subsequent study and practical development. In particular, emerging focus areas comprise artificial intelligence-powered 5G telecommunications, industrial cyber-physical systems, internet of things architectures, and autonomous transport systems. These findings outline where the integration of decentralised ledgers and intelligent algorithms is developing most rapidly.
Understanding how blockchain and artificial intelligence interact helps organisations identify where next-generation digital infrastructure is heading. As industries seek more secure and autonomous digital workflows, tracking these developments highlights priority areas such as connected devices, telecommunications, and industrial automation. This clarity enables researchers and planners to concentrate resources on the most relevant emerging technologies.
The work represents early-stage bibliometric mapping rather than direct technological testing. It indicates potential use cases for technology developers and industrial engineers working on artificial intelligence-powered 5G networks, industrial cyber-physical systems, internet of things deployments, and autonomous vehicles. Because the review synthesises literature trends rather than demonstrating working prototypes, immediate commercial application remains distant, serving primarily to guide research and development strategies in those identified domains.
AI-generated from the published abstract. Always read the original work before citing.
Blockchain and machine learning (ML) has garnered growing interest as cutting-edge technologies that have witnessed tremendous strides in their respective domains. Blockchain technology provides a decentralized and immutable ledger, enabling secure and transparent transactions without intermediaries. Alternatively, ML is a sub-field of artificial intelligence (AI) that empowers systems to enhance their performance by learning from data. The integration of these data-driven paradigms holds the potential to reinforce data privacy and security, improve data analysis accuracy, and automate complex processes. The confluence of blockchain and ML has sparked increasing interest among scholars and researchers. Therefore, a bibliometric analysis is carried out to investigate the key focus areas, hotspots, potential prospects, and dynamical aspects of the field. This paper evaluates 700 manuscripts drawn from the Web of Science (WoS) core collection database, spanning from 2017 to 2022. The analysis is conducted using advanced bibliometric tools (e.g., Bibliometrix R, VOSviewer, and CiteSpace) to assess various aspects of the research area regarding publication productivity, influential articles, prolific authors, the productivity of academic countries and institutions, as well as the intellectual structure in terms of hot topics and emerging trends. The findings suggest that upcoming research should focus on blockchain technology, AI-powered 5G networks, industrial cyber-physical systems, IoT environments, and autonomous vehicles. This paper provides a valuable foundation for both academic scholars and practitioners as they contemplate future projects on the integration of blockchain and ML.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1109/access.2023.3298371
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
