Molecular dynamics simulations using empirical force fields (EFFs) are crucial for gaining fundamental insights into atomic structure and long timescale dynamics of Au nanoclusters with far-reaching applications in energy and devices. This approach is thwarted by the failure of currently available EFFs in describing the size-dependent dimensionality and diverse geometries exhibited by Au clusters (e.g., planar, hollow cages, pyramids). Owing to their ability to account for bond directionality, bond-order based EFFs, such as the Tersoff-type Bond Order Potential (BOP), are well suited for such a description. Nevertheless, the predictive power of existing BOP parameters is severely limited in the nm length scale owing to the predominance of bulk Au properties used to train them. Here, we mitigate this issue by introducing a new hybrid bond order potential (HyBOP), which account for (a) short-range interactions via Tersoff-type BOP terms and (b) long-range effects by a scaled LJ term whose contribution depends on the local atomic density. We optimized the independent parameters for our HyBOP using a global optimization scheme driven by genetic algorithms. Moreover, to ensure good transferability of these parameters across different length scales, we used an extensive training dataset encompasses structural and energetic properties of a thousand 13-atom Au clusters, surface energies, as well as bulk polymorphs, obtained from density functional theory (DFT) calculations. Our newly developed HyBOP has been found to accurately describe (a) global minimum energy configurations at different clusters sizes, (b) critical size of transition from planar to globular clusters, (c) evolution of structural motifs with cluster size, and (d) thermodynamics, structure, elastic properties of bulk polymorphs as well as surfaces, in excellent agreement with DFT calculations and spectroscopic experiments.