Fusing complex data from two disparate sources has been demonstrated to improve the accuracy in quantifying active ingredients in mixtures of pharmaceutical powders. A four-component simplex-centroid design was used to prepare blended powder mixtures of acetaminophen, caffeine, aspirin and ibuprofen. The blends were analyzed by Fourier transform infra-red spectroscopy (FTIR) and powder X-ray diffraction (PXRD). The FTIR and PXRD data were preprocessed and combined using two different data fusion methods: fusion of preprocessed data (FPD) and fusion of principal component scores (FPCS). A partial least square (PLS) model built on the FPD did not improve the root mean square error of prediction. However, a PLS model built on the FPCS yielded better accuracy prediction than PLS models built on individual FTIR and PXRD data sets. The improvement in prediction accuracy of the FPCS may be attributed to the removal of noise and data reduction associated with using PCA as a preprocessing tool. The present approach demonstrates the usefulness of data fusion for the information management of large data sets from disparate sources.