Abstract— Clustering is one of the most popular techniques in unsupervised learning in which data is divided into different groups without any prior knowledge, and for this reason, clustering is used in various applications today. One of the most popular algorithms in the field of clustering is the k-means clustering algorithm. The most critical weakness of k-means clustering is that it is sensitive to initial values for parameterization and may stop at local minima. Despite its many advantages, such as high speed and ease of implementation due to its dependence on the initial parameters, this algorithm is in the optimal local configuration and does not always produce the optimal answer for clustering. Therefore, this paper proposes a new model using the Bald Eagle Search (BES) Algorithm with the Sine Cosine Algorithm (SCA) for clustering. The evaluation of the proposed model is based on the number of iterations, convergence, number of generations, and execution time on 8 UCI datasets. The proposed model is compared with Flower Pollination Algorithm (FPA), Crow Search Algorithm (CSA), Particle Swarm Optimization (PSO), and Sine-Cosine Algorithm (SCA). The results show that the proposed model has a better fit compared to other algorithms. According to the analysis, it can be claimed that the proposed model is about 10.26% superior to other algorithms and also has an extraordinary advantage over k-means. Manuscript profile