## 1 Introduction  
In the current era of technology, artificial intelligence (AI) has become a central research field, significantly influencing numerous scientific disciplines. Simultaneously, agent-based modeling (ABM) has captured researchers' attention as a promising framework for simulating and understanding complex phenomena. In this context, this study proposes a detailed exploration of the synergies between AI and ABM, conducting an extensive analysis of the existing scientific literature. ABM is a powerful simulation technique that characterizes a complex dynamic system through its interacting entities [1, 2, 3]. While ABM provides extensive flexibility for various applications, the complexity of real-world models necessitates the intensive use of computing resources and significant computational time. However, to mitigate computational costs, a metamodel can be constructed to provide insights at a less computationally demanding level. ABM has a range of applications that can be modeled, such as simulating emergency evacuation processes [4, 5], specific transportation applications [6], modeling Grey economic systems [7], financial process modeling [8], analyzing financial contagion effects [9], medicine [10, 11], etc. Although ABM and AI are two distinct fields, they can interconnect through collaboration and complement each other in various ways. Thus, a series of interconnected features can be identified, such as simulating intelligent behavior [2, 12, 13], flexibility and adaptability [2; 14], interconnection with AI technology [15], decision-making and collective intelligence [16], analysis and visualization of complexity [5; 17], as well as innovation and exploration of emergent behaviors in complex systems. Firstly, ABM is considered a modeling method specific to economic cybernetics and complex adaptive systems [17; 18]. On the other hand, the complexity of systems, given their dynamic and sometimes unpredictable nature, and the fact that a complex system is composed of a multitude of entities and agents that interact, result in a large volume of data. The most useful tools for analyzing this large volume of data [19], given the current technological and digital era, are provided by AI algorithms and techniques. The combination of these actions can be achieved through modeling applications offered by ABM and AI algorithms. In this case, ABM focuses on simulating the individual behavior of agents or individual entities and their interactions within the complex system, while AI develops algorithms and models that mimic and reproduce intelligent behavior through machine learning techniques. AI, especially in machine learning, focuses on developing models capable of learning and adapting to new data. By completing this process with ABM, its characteristic flexibility in modeling complex systems and adaptability to real-time changes are recognized. In essence, ABM and AI can complement each other in addressing complex problems, bringing together the advantages of simulating agent-level details and those of automated learning in an integrated and synergistic manner. ABM is a simulation and modeling method that is specific to cybernetics [20]. Cybernetics is an interdisciplinary science that provides a framework for understanding various processes and systems in different fields. AI, as a field of study, offers machine learning algorithms that can be used in managing and analyzing large volumes of data and information, complementing and seamlessly integrating with the methods and frameworks provided by cybernetics. Integrating ABM with AI provides new perspectives for study and applicability in various fields such as sustainable agriculture [21], marketing [22], education [23], biomedical systems [24], agent behavior [25], management [26], etc. ABM has the capacity to simulate agent behavior, while the AI approach analyzes and understands complex patterns, learning from real-time data.