Orogenic magmatic Cu-Ni sulfide deposits represent an important source of copper, nickel, and other critical metals, and their formation is closely related to mantle-derived magma evolution and crust–mantle interaction. The Beishan Orogenic Belt, located in a key tectonic position within the Central Asian metallogenic domain, hosts numerous mafic–ultramafic intrusions formed during the post-collisional extensional stage, many of which are associated with varying degrees of Cu-Ni mineralization. However, uncertainties remain in distinguishing ore-bearing from barren intrusions and in evaluating their mineralization potential. In this study, whole-rock geochemical data of mafic-ultramafic intrusions from the Beishan Orogenic Belt were systematically compiled from published literature to construct a dataset comprising 360 samples. A total of 27 geochemical variables were selected as input features, including major elements (e.g., SiO₂, Al₂O₃, Fe₂O₃, MgO), rare earth elements (La-Lu), and trace elements (e.g., Rb, Nb, Sr, Y). Four machine learning modelswere employed to classify mineralized and non-mineralized intrusions and to evaluate their mineralization potential, such as multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). The results indicate that all models exhibit strong classification performance, with the MLP model achieving the highest accuracy of 97%, followed by XGBoost (94%), RF (91%), and SVM (90%). The modeling results demonstrate that whole-rock geochemical characteristics can effectively capture key metallogenic processes such as magmatic differentiation and crustal contamination, serving as reliable indicators for identifying ore-bearing intrusions. The discrimination method established based on the optimal model enables effective evaluation of the mineralization potential of mafic-ultramafic intrusions in the Beishan Orogenic Belt. This study not only provides a new technical approach for Cu-Ni sulfide exploration in the Beishan region but also offers methodological insights for assessing the mineralization potential of similar orogenic magmatic deposits.