A Scientometric Analysis of Artificial Intelligence Literature for Engineering Problems
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The expansion of Artificial Intelligence (AI) research has generated a massive and complex scientific ecosystem that requires systematic characterization, where no comprehensive studies have analyzed applications for engineering. This work conducts one of the most extensive scientometric analyses to date, encompassing 159,139 publications of the specialized literature indexed in the Web of Science (2005–2024). Using data cleaning, citation normalization (NCII), institutional productivity measures and keyword mining algorithms, the study maps the global evolution of AI research. Results reveal the dominance of Engineering and Computer Science disciplines, with China and the United States leading scientific output. High-impact open-access journals, such as IEEE Access, serve as the main dissemination channels. Emerging topics such as ChatGPT, Big Data, Internet of Things (IoT), and Digital Twins define the current research frontiers. The study provides a macroscopic evidence-based framework for understanding the dynamics of AI research for engineering problems and identifies future directions such as sentiment-based analytics, predictive modeling, and the evaluation of Large Language Models (LLMs) in scientific production. Overall, the main findings highlight AI’s growing role as a multidisciplinary driver of innovation across global research ecosystems.