The Social Internet of Things (SIoT) has revolutionized user experience through various applications and networking services like Social Health Monitoring, Social Assistance, Emergency Alert Systems, and Collaborative Learning Platforms. However, transferring different types of data between the interconnected objects in the SIoT environment, including sensor data, user-generated data, and social interaction data, poses challenges due to their high dimensionality. This paper presents an alternative SIoT method that improves resource efficiency, system performance, and decision-making using the Barnacles Mating Optimizer (BMO). The BMO incorporates Triangular mutation and dynamic Opposition-based learning to enhance search space exploration and prevent getting stuck in local optima. Two experiments were conducted using UCI datasets from different applications and SIoT-related datasets. The results demonstrate that the developed method, DBMT, outperforms other algorithms in predicting social-related datasets in the IoT environment.