Industrial clustering refers to the concentration of related industries or companies in a specific geographic area. On the other hand, knowledge management focuses on capturing, organizing, and leveraging knowledge within organizations. In the context of industrial clustering, knowledge management plays a vital role in facilitating collaboration, innovation, and competitiveness among the clustered companies. In the current research, an approach to knowledge management in the development of industrial clusters is proposed. Thus, an integer binary mathematical programming model is presented to select the programs with the highest knowledge-enhancing effect. The proposed model allocates the most important programs in its specialized portfolios. To validate the proposed mathematical model, five types (program portfolio) including training workshop, training course, industrial tour visiting industries, exhibition visiting tour, and participating in the exhibition are considered for ten production units. Each of the programs improves the six types of knowledge of its employees in the fields of design, production, purchasing, finance, marketing, and administration in three skill levels (low, medium, and high). According to the results obtained from solving the model with the GAMS software, the programs are assigned to each cluster. The most important finding of the research is that if the programs are implemented in each production unit separately, the improvement of knowledge is less than when they are implemented as clusters. Therefore, it can be concluded that the proposed model has a suitable efficiency in creating knowledge alignment in the organization with the approach of industrial clusters. Moreover, the results indicate that if these programs are implemented separately, the cost of knowledge promotion would increase significantly. Knowledge management plays a crucial role in nurturing collaboration, innovation, and competitiveness within industrial clusters. It helps cluster members unlock the potential of shared knowledge and resources, leading to mutual benefits and the overall development of the cluster.