This study aims to develop a comprehensive model to identify and analyze the factors influencing female customers’ purchasing behavior in the fashion and apparel industry, with a focus on Industry 4.0 technologies. In terms of purpose, the research is applied, and in terms of method, it follows a descriptive–analytical approach implemented through a mixed (qualitative–quantitative) design. The study population consisted of experts in marketing, consumer behavior, smart/Industry 4.0 technologies, and the fashion and apparel sector. A total of 15 experts were selected using non-probability, purposive, and judgmental sampling. The research process was conducted in three main stages. First, 30 initial drivers were extracted through a systematic literature review and expert consultations. Second, the factors were screened using the Fuzzy Delphi method, and 10 key factors with a fuzzy score above 0.7 were retained. Third, the mutual effects among the selected factors and their structural relationships were examined using cross-impact analysis in MICMAC software. The findings indicate that women’s purchasing behavior in this industry is a multidimensional and dynamic outcome of interacting technological, psychological, social, and experiential dimensions. Structural analysis highlights that factors such as AI-based intelligent personalization, virtual fitting-room experience, perceived risk reduction, and privacy of biometric data play pivotal roles in shaping customers’ decision-making. The study concludes that digitalization and the adoption of advanced technologies significantly transform women’s shopping experience; however, their effectiveness is not necessarily linear or universally positive. Therefore, brand success depends on user-centered design, reducing cognitive load, and ensuring robust privacy protection to enhance trust, satisfaction, and customer loyalty