The aim of the present study is to design an artificial intelligence-based marketing management model in the steel industry. This research is applied in terms of objective and utilizes a mixed-methods (qualitative-quantitative) approach. The statistical population consists of 20 experts in the field of business management, including university professors, engineers, behavioral science researchers, and industrial managers familiar with marketing management and artificial intelligence, who were selected using purposive judgmental sampling. Deep expert interviews were used to collect qualitative data. Data grounded theory was employed to analyze the findings in the qualitative section, while PLS was used in the quantitative section.The results of the study indicate that the AI-based marketing management model comprises 6 key dimensions, identified as follows: the core phenomenon (including 4 dimensions: data-centricity, automation, predictive analysis and competitors, and innovation and learning culture); causal factors (including 3 dimensions: organizational factors, customer factors, and digital factors); contextual conditions (including 2 dimensions: technology and infrastructure, and customer behavior and expectations); strategies (including 3 dimensions: investment in AI technologies, customer experience personalization, and digital marketing strategies); and finally, outcomes (including 4 dimensions: economic outcomes, operational outcomes, customer-centric outcomes, and competitive outcomes). In the quantitative section, path coefficient and significance testing were conducted for the variables, confirming that the AI-based marketing management model possesses desirable significance and validity.