The aim of this research is to design and explain a science map in the field of customer behavior based on electroencephalogram (EEG) data with an emphasis on artificial intelligence and machine learning. With the expansion of interdisciplinary studies in neuromarketing, behavioral neuroscience, and smart technologies, the need to analyze the knowledge structure of this field and identify related conceptual clusters is increasingly felt. For this purpose, the present study uses a scientometric approach and synonym analysis and, by extracting data from the reputable scientific databases Scopus and Web of Science, has identified a network of frequently occurring keywords and basic concepts in this field. In the first step, general and specific keywords including terms such as EEG, neuromarketing, artificial intelligence, deep learning, customer decision-making, cognitive response, and neural network analysis were collected using a systematic review method. Then, scientific data related to these terms were collected from indexed articles between 2010 and 2024, and after initial refinement, a thesaurus network analysis was performed using VOSviewer software. The research findings indicate that the knowledge structure of this field can be categorized into five main clusters, including neuroscience and brain response analysis, artificial intelligence and predictive technologies, neuromarketing and branding, consumer psychology, and cognitive tools and customer behavior measurement. These clusters demonstrate the growing trend of interaction between neuroscience, EEG data analysis, and machine learning algorithms, and represent new approaches to understanding consumer behavior. The results showed that concepts such as EEG, artificial intelligence, machine learning, neuroscience, and consumer decision-making had the highest co-occurrence and conceptual weight in the scientific literature. Science mapping also visually identified semantic connections and future research directions and revealed existing knowledge gaps. This research, while contributing to the neuromarketing and cognitive neuroscience literature, will pave the way for designing more accurate predictive models for consumer behavior in the context of smart technologies. Finally, based on the results obtained, it was suggested that the development of open laboratories and cooperation between industry, academia, and research institutions in this field be strengthened to both produce real and reliable EEG data and provide a platform for the practical application of AI models in marketing. Such an approach can lead to improved customer experience, advertising effectiveness, and strategic decision-making in marketing, and ultimately provide a valuable scientific-practical framework for researchers and market activists.