Exploration for critical minerals, such as beryllium (Be) and uranium (U), requires accurate reserve assessment, for which drill core analysis is essential. Techniques like laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence spectroscopy (XRF) are widely used for rapid core analysis but have some limitations. LIBS suffers from poor sensitivity for low-concentration U, while XRF cannot detect Be. Furthermore, matrix effects in both techniques hinder the accurate simultaneous quantification of Be and U. We introduce a novel LIBS-XRF method for the simultaneous measurement of Be and low-concentration U. The methodology involves first analyzing samples with XRF and LIBS. Subsequently, a support vector machine (SVM) algorithm classifies the samples based on the XRF data. A separate predictive model is then developed for each category. A basic linear model is constructed using the spectral line of the target elements as the dominant factor based on dominant factor (DF) modeling strategy, and machine learning algorithms are then used to compensate for the residuals of this basic model. Tests on ore cores demonstrated that this method significantly reduces quantification errors. The achieved mean relative errors were 7.58% for Be and 7.02% for U. These results represent improvements of 61.42%/77.20% and 69.77%/72.48% over conventional unclassified and experience-based methods, respectively. This work is the first to use a LIBS-XRF approach for the highly accurate and simultaneous detection of Be and low-concentration U in ore cores, proving its high practical utility for this application.