## 1 Introduction  
Digital transformation in e-commerce has gained significant attention in recent years, driven by advancements in context-aware computing and Artificial Intelligence (AI) driven personalization [1]. E-commerce platforms serve as digital ecosystems that facilitate online transactions by providing businesses with essential tools to manage customer interactions, payments, and digital storefronts [2]. These platforms operate across multiple devices, enabling users to access services at home, in the office, or anywhere. They offer a wide range of services in domains such as healthcare, transportation, retail, and supply chain management, while leveraging cutting-edge technologies to optimize user engagement and decision-making. The increasing role of technology in e-commerce demands rigorous enhancement of User Experience (UX) design, particularly as AI emerges as a transformative tool in e-commerce practices. Integrating AI in e-commerce opens new possibilities for dynamic personalization. It also improved customer experiences, making applications more interactive, adaptive, and data-driven. Several studies highlight challenges related to automation, UI refinement, and the practical implementation of predictive models, which affect the accuracy of user behavior in specific scenarios [3; 4; 5]. [PERSON] et al. [3] introduced a context-aware heterogeneous graph attention network to predict user behavior, emphasizing the role of contextual information in user engagement. [PERSON] et al. [4] explored Reinforcement Learning (RL) for continuous UI layout optimization based on user feedback. While these approaches present promising directions, they often lack predictive and priority guidance for user interactions, particularly through multi-agent RL-based adaptation, user clustering, and generative AI. Additionally, existing methods face challenges in scaling across different platforms and cannot quickly adapt to evolving user needs. Model-Driven Development (MDD) remains underutilized in AI-enhanced UX design, limiting the ability to formalize, reuse, and automate UI/UX adaptations. These limitations hinder the development of intelligent interfaces capable of learning from and responding to user behavior over time. The proposed framework addresses these limitations by combining generative AI with model-driven principles, enabling real-time UX optimization and enhanced adaptability. Furthermore, studies indicate that nearly 90% of e-commerce platforms fail within the first three months of operation, due to suboptimal UX workflows and an inability to refine user interactions based on behavioral data. Many businesses outsource development, leading to a disconnection between UI design and market-specific user expectations, which exacerbates usability issues. This misalignment highlights the need for AI-driven, adaptive design methodologies that can bridge the gap between user expectations and digital storefront effectiveness. This paper presents a novel approach to e-commerce by integrating generative AI with the Model-Driven Development approach (AI-MDD-UX) to enhance engagement, personalization, and user experiences. The methodology combines KMeans++ for user clustering, Generative Adversarial Networks (GANs) for UI generation, and RL agents for adaptive interface optimization. The structured multi-level MDD framework translates user behavior insights into dynamic UI interactions, enabling real-time personalization and continuous design improvements. The key contributions of this study are  
1. We present a novel hybrid approach that combines generative AI and MDD to ensure real-time UI/UX optimization in e-commerce. AI leverages KMeans++ for behavior-driven user clustering, GANs for automated UI generation, and RL agents for continuous interface adaptation, while MDD provides the structural foundation for consistency and automated adaptation. 2.