To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations that evolve particles under gravity by estimating particle-particle interactions (N-body simulations) are computationally expensive and prohibitve to scale to the large volumes and resolutions necessary for the upcoming datasets. Moreover, modeling the distribution of galaxies typically involves identifying collapsed and bound dark matter structures called halos. This is also a time-consuming process for large N-body simulations, further exacerbating the computational cost. In this study, we introduce CHARM, a novel method for creating mock halo catalogs by matching the spatial and mass statistics of halos directly from the large-scale distribution of dark matter density field. We develop multi-stage neural spline flow based networks to learn this mapping directly with computationally cheaper, approximate dark matter simulations instead of relying on the full N-body simulations. We validate that the mock halo catalogs have same statistical properties as obtained from traditional methods. Our method effectively provides a speed-up of more than a factor of 1000 in creating reliable mock halo catalogs compared to conventional approaches. This study represents a major first step towards being able to analyze the non-Gaussian and non-linear information from current-generation surveys using simulation-based inference approaches on the massive scales of upcoming surveys.