In the last century, astronomical breakthroughs, particularly on Dark Matter and Dark Energy, have reshaped our understanding of the Universe. Despite comprising 95% of the Universe, these enigmatic components remain mysterious. Upcoming radio astronomical surveys, such as SKA and HIRAX, will promise unprecedented datasets with unique power to improve our understanding of this dark sector of the Universe. This work focuses on developing a forward modeling pipeline for simulating SKA and HIRAX observations. Utilizing the PINOCCHIO code for Dark Matter halos and a Halo Model-based approach for adding neutral hydrogen onto halos, this pipeline generates physically motivated catalogs of neutral hydrogen, which are post-processed with a telescope simulator that incorporates systematic effects specific to these arrays. The goal is to generate scalable and efficient simulations for statistical forecasts and cosmological studies, leveraging GPU-enabled implementations of the pipeline as well as resources from the CSCS Piz Daint supercomputer.