The rapid growth of data has posed significant challenges for machine learning, particularly in the context of unlabeled datasets. While clustering techniques offer potential solutions, the k-means algorithm often suffers from local minima. This study introduces hybrid approaches combining the Salp Swarm Algorithm (SSA) and Harris Hawks Optimization (HHO) with different initialization strategies (random, K-means, K-means++) to enhance clustering performance. Experimental results on ten UCI benchmark datasets demonstrate that these hybrid approaches significantly outperform traditional K-means and K-means++ methods in minimizing within-cluster distances. This suggests improved cluster quality, which can be valuable for various downstream applications.