<p>Accurate identification and quantification of radionuclides in soil are essential for environmental monitoring, nuclear safety, " and emergency response. Machine-learning methods operating on full-spectrum gamma-ray data offer a promising alternative to traditional peak-based analysis, but their development is impeded by the lack of large, publicly accessible training datasets that reflect realistic soil matrices and high-resolution HPGe (High-Purity Germanium) detector characteristics. In this work, we introduce the first open Monte Carlo N-Particle transport (MCNP)-based HPGe soil gamma-ray spectroscopy dataset, comprising 6000 simulated spectra covering 41 prevalent radionuclides across diverse activity levels. We benchmark four regression approaches—Ridge Regression, Extreme Gradient Boosting Regression, Multilayer Perceptron, and Convolutional Neural Network—on quantification tasks using a held-out test set. Linear and ensemble methods achieve robust baselines, successfully predicting over 95% of isotopes within ± 15% relative error, whereas the tested deep-learning architectures exhibit greater variability on low-intensity and overlapping-peak nuclides. These results demonstrate the dataset’s utility for reproducible research and highlight significant opportunities for architectural innovations and domain-adaptation strategies to enhance deep-learning performance. We anticipate that this resource will catalyze the development of more accurate, generalizable machine-learning solutions for multi-isotope activity quantification in environmental applications.</p>