Broadband noise is generated by flow turbulence. This conversion process from turbulence to acoustic energy is particularly strong when the turbulent flow interacts with solid surface such as the wing leading or trailing edge. Scale-resolving simulations required to resolve the noise generating turbulent fluctuations are often computationally prohibitive, especially for high Reynolds number wall-bounded flows. The Fast Random Particle Mesh (FRPM) method predicts broadband noise by generating synthetic turbulent velocity fields based on the turbulence kinetic energy and dissipation rate modeled by the Reynolds-Averaged Navier-Stokes (RANS) equations, thus circumventing the often-exorbitant computational cost of scale-resolving simulations otherwise required to capture broadband noise sources. In FRPM, spatiotemporal white-noise is spatially convolved with user-defined filter kernels, enabling the reproduction of two-point turbulence statistics, compared to other stochastic techniques that primarily fit turbulence power spectra directly through random Fourier modes. The solenoidal nature of the fluctuations generated by FRPM helps avoid the creation of spurious sound sources. The broadband noise source synthesized by FRPM is propagated and interact with the solid surface by solving a pressure-Poisson equation.