In this research, we focus on the detection and monitoring of iron content in mining areas, which is of great significance in many hyperspectral imaging (HSI) studies that can be used to assess the advantages and disadvantages of the soil environment. Compared with the traditional grid sampling and interpolation methods, the unmanned aerial vehicle (UAV) hyperspectral inversion method can be used to quickly account for the large-area inversion of iron content and draw thematic maps of iron concentration in a given area suitable for mining for deposits. In this paper, we propose a novel classification methodology for selecting the optimal model for the UAV hyperspectral inversion of iron content using mathematical and computational modeling. Through the cross-validation comparison of three regression models, the most suitable model is found for the inversion of soil iron content. In addition, we also analyzed and compared the effects of different feature sets, namely, band selection, principal component analysis (PCA), and minimum noise fraction (MNF), on the model accuracy. Our experiments have proved that among many inversion models and feature combinations, the partial least squares regression (PLSR) model combined with band selection, PCA feature extraction, and MNF feature extraction can greatly improve the inversion accuracy of iron concentrations in the identified areas.