The Radial Basis Function (RBF) interpolation method has emerged as a powerful geostatistical tool for mineral resource estimation, offering flexibility and robustness in modeling complex orebody geometries and grade distributions. Unlike traditional estimation techniques such as Inverse Distance Weighting (IDW) or Ordinary Kriging, RBF utilizes a smooth mathematical function that fits through known data points while minimizing interpolation error. This property enables the method to capture both global and local grade variability with high precision. In this study, the RBF approach was applied to a gold deposit using drillhole assay data to generate a continuous three-dimensional grade model. Various kernel functions—such as multiquadric, Gaussian, and thin-plate spline—were tested to optimize model accuracy and performance. Cross-validation results demonstrated that the RBF model effectively reproduced spatial grade trends and provided smoother, geologically realistic surfaces compared to conventional methods. The resulting block model was validated against composited data and geological interpretations, showing strong correlation and reduced estimation bias. The RBF method proves particularly advantageous in deposits with irregular sampling patterns or non-stationary grade distributions. Consequently, RBF interpolation represents a valuable alternative for modern resource estimation workflows, especially when integrated within advanced 3D geological modeling software such as Leapfrog Geo.