The isoflavones in the cotyledon of soybean seeds mimic human estrogen in structure, conferring them complex effects on health. Their regulation represents a major challenge for the sustainable breeding of new varieties with lower levels of potential endocrine disruptors. To develop a rapid, nondestructive, and eco-friendly analysis method, this study explores how sample grinding affects the results of near-infrared spectroscopy (NIRS) and the preprocessing methods. The prediction of the daidzein and genistein content would help the specific reduction in isoflavones in the cotyledon without harming seed development. The potential of a nonlinear approach (artificial neural network) is also compared with the more conventional partial least squares (PLS) regression. The isoflavone content of cotyledons from 529 soybean samples (65 genotypes) was quantified by HPLC, and the NIR spectra of these samples were collected using a Brucker multi-purpose analyzer. The spectra of whole and ground cotyledons were also collected for 155 samples. The results show that grain fragmentation improves the model calibration, although spectral preprocessing can harmonize this effect. Although the best PLS regression in cross-validation did not suffice to quantify the daidzein and genistein percentages, the artificial neural network (ANN) approach allowed us to develop much more reliable models than PLS. The performance of ANNs in external validation is remarkable in terms of both precision and applicability (R 2 = 0.89 and a ratio of prediction to deviation of 2.92), making ANNs suitable in the breeding context for screening soybean grains regarding their isoflavone content.