To investigate the elemental differences in beef from different regions of China and construct a reliable origin traceability method, the contents of 52 elements in beef samples from seven production regions (Shandong, Yunnan, Ningxia, Xinjiang, Jiangsu, Gansu, and Sichuan) were systematically quantified using inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectrometry (ICP-OES). An original dataset was constructed based on the elemental contents, and a discriminant model for beef origin was developed by applying multivariate statistical and machine learning methods, including multivariate analysis of variance (MANOVA), random forest (RF), linear discriminant analysis (LDA), and cosine similarity algorithms. Through feature importance evaluation and variance analysis, 27 key elements (Sc, Co, K, Se, Rb, V, Ba, As, Sn, Pb, Na, Cu, Mg, Ag, Al, P, Ni, Tl, Fe, Ti, Zn, Mn, Cd, Ca, Cs, Sb, and Mo) with significant regional differences were identified and used to construct the core feature space of the model. Subsequently, 12 external validation samples were introduced for origin identification, and the results showed a discrimination accuracy of 91.67%, confirming the feasibility and robustness of the established model. The findings demonstrated that significant differences exist in the elemental composition of beef from different regions, and elemental fingerprints can serve as reliable indicators for origin traceability. This achievement provides a theoretical basis for improving beef origin authentication and ensuring the quality and safety of meat products.