Harris Hawks optimization (HHO) is a recent population-based optimization algorithm that has been recently proposed to address several different problems. Sometimes, poor exploitation (intensification) ability influences the performance of Harris Hawks optimization. This chapter proposes a new hybridization strategy, namely, hybrid Harris Hawks optimization with differential evolution (DE) (H-HHO), to tackle the data clustering problem. The proposed method attempts to improve the local (exploitation) search skill of the Harris Hawks’ optimization to achieve the optimal solution. The proposed H-HHO is handled by adding local and global search operators from the differential evolution. This idea is employed to improve the search capabilities in Harris Hawks optimization to explore the optimal solution. Thus, its solutions’ positions move near the global optimal. Experiments are conducting utilizing four conventional benchmark datasets from the Machine Learning Repository (UCI), which is generally utilized in the field of machine learning. The results revealed that the proposed hybrid method (H-HHO) provided very distinct clusters, particularly in massive datasets. Moreover, the proposed H-HHO got a better convergence rate. It can overwhelm the other similar algorithms by getting better results according to the clustering processes.