We present numerical models for predicting five key electronic properties of zinc sulfide \( (ZnS) \) nanoclusters: binding energy per \( ZnS \) unit, static dipole polarizability, HOMO-LUMO gap, vertical ionization potential, and chemical hardness. These models employ four graph-based topological indices (Wiener, hyper-Wiener, second-order connectivity, and Szeged) alongside cluster size \( n \). The models offer substantially reduced estimation errors and strong predictive performance. The results demonstrate that numerical models provide efficient routes for estimating \( ZnS \) nanocluster properties, facilitating rapid screening and guiding experimental efforts.