This article investigates a novel data fusion method to predict clay content and cation exchange capacity using visible near-infrared (visNIR) spectroscopy, portable X-ray fluorescence (pXRF), and X-ray diffraction (XRD) techniques. A total of 367 soil samples from two study areas in regional Australia were analyzed and intra- and interarea calibration options were explored. Cubist models were constructed using information from each device independently and in combination. pXRF produced the most accurate predictions of any individual device. Models based on fused data significantly improved the accuracy of predictions compared with those based on individual devices. The combination of pXRF and visNIR had the greatest performance. Overall, the relative increase in Lin's concordance correlation coefficient ranged from 1% to 12% and the corresponding decrease in root-mean-square error (RMSE) ranged from 10% to 46%. Provision of XRD data resulted in a decrease in observed RMSE values, although differences were not significant. Validation metrics were less promising when models were calibrated in one study area and then transferred to the other. Observed RMSE values were ∼2 to 3 times larger under this model transfer scenario and independent use of XRD was found to have the best overall performance.