The rise of the digital economy has catalyzed the need for advanced personalized recommendation systems to improve user engagement and satisfaction. However, existing recommendation methods often encounter challenges related to data sparsity, accuracy, and computational efficiency. This chapter proposes a novel approach that integrates artificial intelligence (AI) techniques with multi-objective optimization to address these issues effectively. By leveraging deep learning models and optimization algorithms, our method enhances recommendation accuracy while balancing various system objectives. Experimental results demonstrate a significant improvement in recommendation precision, recall, and computational efficiency compared to traditional methods. This research contributes to the field by offering an optimized, scalable solution for real-time recommendation in the digital economy.