Dataset Abstract This repository provides 3D reddening maps of the Magellanic Clouds with Python query tools. The dataset contains spatially-resolved extinction measurements across both Large and Small Magellanic Clouds, stored in GeoJSON format with pre-computed linear extinction coefficients. The accompanying tools enable efficient E(V-I) queries for any given coordinate and distance, supporting both single-point calculations and batch processing operations. All data and tools are available in this repository, with detailed usage instructions provided in the README file. Article Abstract We present the first three-dimensional reddening maps of the Large and Small Magellanic Clouds (LMC and SMC) constructed using fundamental-mode RR Lyrae stars from the OGLE-IV survey. By applying a period–amplitude–color relation and a period–luminosity–metallicity calibration in the OGLE photometric system, we derive intrinsic colors, color excess $E(V-I)$, and photometric distances for more than 20,000 RRab stars in the LMC and 3,000 in the SMC. Spatial variations in reddening are modeled using an adaptive quadtree scheme, where robust reddening–distance relations are fit within each partition and distances are iteratively updated to achieve self-consistency. The resulting maps reveal resolved dust structures across both galaxies, including steep reddening gradients in the central LMC and flatter profiles in the SMC. The construction of the three-dimensional reddening maps further reveals that high-extinction regions exhibit reddening behavior inconsistent with a uniform extinction law, implying localized variations in dust properties. The final maps comprise 205 partitions for the LMC and 67 for the SMC, and are released together with a Python-based query tool and GeoJSON data products. These 3D maps provide a foundation for distance-dependent reddening corrections and for probing the structure and physical conditions of the Magellanic interstellar medium, and future high-precision and cadence RR Lyrae sample from Gaia DR4 will support higher-resolution mapping and deeper exploration of dust substructure.