Hexagonal boron nitride (BN) has shown great potential in lubrication, electronic devices, sensors, and as an additive for cosmetic products due to its light weight, thermodynamic and chemical stability, great strength-to-weight ratio, and increased resistance to oxidation. Its used in microfluidic and nanofluidic applications at the molecular level demands accurate force-field parameters to describe the interactions between BN and water molecules. In this study, particle swarm optimization (PSO) and machine learning (ML) were coupled with molecular dynamics (MD) simulations to accelerate the development of Lennard-Jones (LJ) parameters, which are used to describe the non-bonded interactions between a water droplet and a sheet of hexagonal BN. Three commonly used water models, namely, SPC, SPC/Fw, and SPC/E were employed to describe water, while the BN sheet was modelled using REBO potential. The LJ parameters were optimized to reproduce the nanoscopic contact angle of water on the BN sheet.