Commercial fuels are characterized by parameters, such as research octane number and contents of additives, such as ethanol, ethyl-t-butyl ether, ethyl-tert-methyl ether, olefins, etc. For fast and easy parameter determination without the need for sample preparation, we used compact and benchtop near-infrared (NIR), proton nuclear magnetic resonance (H-1 NMR) at 80 MHz, and two Raman spectrometers to predict selected relevant fuel parameters of 179 samples known from CFR motor and norm-compliant analyses. Repeatability and reproducibility criteria according to ASTM and ISO norms served as goodness of prediction measures. The prediction relied on partial least squares regression type 1 yielding one target parameter and type 2 yielding simultaneously n target values. While PLS-1 provided more accurate results, PLS-2 might be further applicable to RON and oxygenated additive content determination. Among the methods applied, benchtop Raman and H-1 NMR performed best. Low-, mid-, and high-level data fusion were applied to transform pretreated subspectra from up to three individual techniques to result in pseudo-spectra, combined score matrices, or decision models, which further improved the accuracy of the RON prediction. Best results for RON were obtained with mid-level fusion of NIR, NMR, and Raman data yielding 63% of the predicted values within reproducibility of 0.2 and up to 97% within repeatability of 0.7 RON.