Reverse Monte Carlo (RMC) modeling has emerged as a powerful computational tool for reconstructing atomic configurations from experimental data, including X-ray and neutron scattering. By iteratively refining atomic positions to match measured structure factors, RMC provides insights into short- and intermediate-range order, structural motifs, and hidden correlations. The method has successfully been applied to liquids, covalent and metallic lasses, and disordered crystals, advancing our understanding of many complex structural henomena.