In the dynamic landscape of digital marketing, organizations face the challenge of simultaneously optimizing multiple interdependent objectives, such as maximizing audience reach, enhancing engagement, and minimizing costs. This study proposes an intelligent multi-criteria optimization algorithm that integrates the Analytic Hierarchy Process (AHP) and Genetic Algorithms (GA) to address these challenges systematically. The AHP framework establishes objective weights based on strategic priorities, while the GA iteratively refines marketing budget allocations across various channels. A simulation conducted on data for five marketing channels demonstrated that the algorithm successfully prioritized budget allocation towards the most effective channel - Social Media - achieving optimal reach (1.0), engagement (0.977), and cost efficiency (0.258). The convergence analysis revealed consistent improvements across generations, underscoring the algorithm’s ability to balance conflicting objectives effectively. Comparative analysis indicated a 15% improvement in overall campaign performance and a 10% reduction in costs compared to traditional single-objective optimization approaches. These findings suggest that the proposed algorithm provides a scalable and adaptable tool for data-driven decision-making in complex digital marketing environments.