<p>In the competitive B2B marketing environment, optimal decision-making requires intelligent and data-driven models that can respond to real-time market changes. This research presents an innovative framework for multi-objective optimization in B2B marketing that uses digital twins and real-time data to maximize profits, reduce marketing costs, and increase customer engagement. To achieve these goals, two algorithms, Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Whale Optimization Algorithm (WOA), provide a combination of high convergence speed and accuracy in finding optimal solutions. The results show that NSGA-II performs better when quick decision-making is required, while WOA provides more optimal solutions in some cases. Also, examining the role of digital twins showed that the proposed model can reduce additional costs and improve decision accuracy by continuously adjusting marketing strategies. Sensitivity analysis also confirmed that increasing marketing budgets and improving customer engagement rates directly impact growing profitability. The results of this research show that the proposed optimization framework, by integrating digital twins and multi-objective meta-heuristic algorithms, has the ability to improve resource allocation and performance indicators in dynamic and uncertain environments. This approach can be used in areas such as B2B digital marketing, supply chain management, and resource allocation in online service systems.</p>