## 1 Introduction
Thus, the interconnection between the two bridges the gap with the aim of contributing to more informed and efficient decision-making, leading to the development of more flexible and resilient systems/models. Additionally, AI is employed to analyze large volumes of data, and in conjunction with modeling individual and collective behavior through ABM, it enables more accurate forecasting and rapid identification of specific trends [27; 28; 29]. [PERSON] and [PERSON] [25], in their study, provided a detailed presentation of the relationship between ML technology and agent-based approaches. Based on their analysis, they concluded that the main framework researchers can employ to address specific challenges identified individually in both methods is to use ML models to simulate agent behavior in their ABMs. [PERSON] et al. [30] conducted a study in which they examined challenges arising from agent behavior governed by rules derived from their bounded rationality and data scarcity. The authors addressed this challenge by incorporating domain expert knowledge with machine learning techniques. Other investigations focus on the applicability and interconnection between ABM and AI in the medical field. For example, in their research, [PERSON] et al. [24] provided examples of how ABM and ML are integrated into various contexts covering spatial scales, including multicellular biology. The primary aim of their research was to use published studies as a guide to identify suitable approaches to machine learning based on specific types of ABM applications, considering the scale of the biological system and the characteristics of the available data. Another study combines ABM with AI by constructing a metamodel that integrates ABM with random forest regression and neural networks. This approach has highlighted the benefit of reducing the number of required ABM simulations to validate a model [1]. Models and agent-based simulations are commonly encountered in various fields, providing a means to study systemic patterns resulting from individual behavior and interactions. Achieving the behavioral accuracy required for predictive models represents one of the significant challenges of ABM, and the use of learning algorithms can contribute to enhancing this accuracy in behavioral modeling [31]. The primary purpose of this study is to conduct a comprehensive bibliometric analysis of the utilization of AI in ABM applications. By exploring the existing scholarly literature, the aim is to identify patterns, trends, and the interconnected landscape between these two dynamic fields. This investigation seeks to provide insights into the evolution, challenges, and opportunities at the intersection of AI and ABM, offering a foundation for future research directions in this interdisciplinary domain. Additionally, our study can make a significant contribution by highlighting novelties in the field, identifying key research directions, and providing a comprehensive perspective on the relationship between AI and ABM. As [PERSON] et al. [32] suggest, the bibliometric analysis best fits the situations in which one aims to present the state of intellectual structure and emerging trends in a research field. The methodology involves a meticulous bibliometric approach, leveraging a well-established database, namely WoS [33]. Selection criteria include articles related to ABM and AI, with a focus on titles, abstracts, and keywords. The chosen data set is then refined through language and document type filters, ensuring a targeted and relevant sample. The bibliometric analysis utilizes the Bibliometrix platform in RStudio [34] and VOSviewer software (version 1.6.20) [35], employing techniques such as keyword co-occurrence clustering and network visualization to unveil patterns and connections within the literature. This study innovatively combines bibliometric analysis with the exploration of AI and ABM, shedding light on the dynamics of these fields through a quantitative lens.