A bibliometric review of machine learning applications in multidomain operations: a decade of progress

Multi-domain operations have grown in complexity with the integration of diverse operational theaters such as land, air, sea, space, and cyberspace. Artificial intelligence and machine learning have become essential tools for enhancing decision-making, operational planning, and autonomous system management in this evolving defense landscape. This paper presents a comprehensive bibliometric analysis of AI and ML applications in multi-domain operations from 2013 to 2024. Data were gathered from multiple academic databases and analyzed using VOSviewer, which enabled the mapping of keyword co-occurrences, citation networks, and influential research clusters. The analysis identified thematic clusters that encompass foundational AI/ML techniques, advanced algorithmic innovations such as adversarial and federated learning, optimization methodologies, deep learning frameworks, and systems supporting command and control. Emerging trends also include cybersecurity integration and human-machine teaming, underscoring the dynamic evolution of the field. These findings offer critical insights into the intellectual structure of research at the intersection of technology and military strategy. They provide a foundation for future studies aimed at developing secure, adaptive, and efficient AI-driven systems capable of addressing the challenges inherent in complex, multi-domain environments.