<![CDATA[The rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies has transformed manufacturing systems under the Industry 4.0 paradigm, enabling data-driven process optimization, predictive decision-making, and intelligent production management. Despite substantial growth in this research domain, previous bibliometric studies reported limited visibility of the Industrial Internet of Things (IIoT) and industrial operators within the AI/ML-based process optimization literature. This study aims to examine the evolution of these research themes and assess how the knowledge structure of the field has developed during the transition from Industry 4.0 to Industry 5.0. A bibliometric analysis was conducted using 362 publications retrieved from Dimensions.ai covering the period 2020–2026. Bibliometric performance indicators were analyzed using Bibliometrix (R), while science mapping and keyword co-occurrence analyses were performed using VOSviewer 1.6.20. The results reveal a continuous increase in publication output and the emergence of six major thematic clusters. AI and Smart Factory technologies remain the dominant research themes, followed by Smart Manufacturing and Cyber-Physical Systems. The analysis further shows that IIoT has evolved into a distinguishable thematic component connected to industrial connectivity, edge computing, and sensor infrastructures. In addition, a new human-centered cluster has emerged, characterized by concepts such as Operator 4.0, human-in-the-loop systems, collaborative robotics, and human-centered AI. Although both IIoT and operator-related themes have gained visibility, their thematic prominence remains lower than that of the dominant AI and smart manufacturing clusters. The findings indicate a gradual shift toward a more integrated manufacturing paradigm that combines intelligent algorithms, industrial connectivity, and human expertise, reflecting the broader transition from Industry 4.0 to Industry 5.0.]]>
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