Abstract Multi-label classification is a significant challenge in machine learning, especially as the dimensionality of the problem increases. As the number of dimensions grows, the performance of traditional classification algorithms often degrades substantially. Feature selection is a key technique for reducing dimensionality in multi-label scenarios, operating as a non-parametric process. Despite its importance, feature selection remains a complex issue without straightforward solutions, and various approaches using AI and evolutionary algorithms have been proposed to tackle it. However, these methods typically suffer from reduced efficiency and slower convergence as the dimensionality increases, due to the expanding search space. To address this issue and enhance convergence speed, this article introduces a hybrid AI solution that combines a binary particle swarm optimization algorithm with a local search strategy specifically designed for multi-label feature selection. Within this local search strategy, feature fusion plays a crucial role, where features are merged based on their relevance and correlation with the problem’s output. These fused features are divided into two categories: those directly associated with the problem class and those that are similar to the problem class but distinct from other feature fusions. By leveraging this categorization, the particle swarm optimization technique is augmented with a local operator that removes redundant feature fusions and refines each solution. By incorporating this operator, the proposed method achieves superior convergence speed compared to previous algorithms in the field. The performance of the proposed method was evaluated across several datasets against some of the most widely used feature fusion selection algorithms. The experimental results demonstrated the proposed method’s accuracy and efficiency, validating its effectiveness in multi-label feature selection.