<p>This research presents a multi-objective fuzzy digital twin framework for simultaneous optimization of dynamic pricing and product portfolio layout/mix in metaverse-based retail environments. In the proposed framework, avatar behavior including observation, movement, and interaction is captured as behavioral input of the digital twin, and demand is modeled using triangular fuzzy numbers to account for uncertainty arising from user behavior in the 3D environment. The decision-making model is formulated as a multi-objective optimization problem including profitability, customer engagement, demand risk, and value of complementarity/substitution relationships and is solved using NSGA-II and MOPSO algorithms. To assess the sustainability, each scenario is analyzed in 30 independent runs and the quality of the Pareto front is examined with indices such as Hypervolume and IGD. The results show that the proposed framework significantly improves business indicators, including operating profit, conversion rate, and customer engagement index, compared to the baseline policy, while maintaining the stability of decisions under conditions of demand uncertainty. The main contribution of the research is to provide an integrated and applicable framework for connecting digital twin behavioral data, fuzzy demand modeling, and multi-objective optimization in price-basket decisions in metaverse environments.</p>