Petroleum-derived gasoline is still the most widely used liquid automotive fuel for ground vehicles equipped with spark-ignition engines. One of the most important properties of gasoline fuels is their antiknock performance, which is experimentally evaluated via the octane number (ON). It is widely accepted that the standard methods for ON measuring (RON: research octane number and MON: motor octane number) are very expensive due to the costs of the experimental facilities and are generally not suitable for field monitoring or online analysis. To overcome these intrinsic problems, it is convenient that the ON of gasoline fuels is estimated via faster methods than the experimental tests and allows for acceptable results with acceptable reproducibility. Various ON prediction methods have been proposed in the literature. These methods differ in the type of fuels for which they are developed, the input features, and the analytical method used to underlie the link between input features and ON. The aim of this work is to develop and evaluate three empirical methods for predicting the ON of petroleum-derived gasoline fuels using MIR spectra, GC-MS, and routine test data as input features. In all cases, the chosen analytical method was partial least squares regression (PLSR). The best performance for both MON and RON prediction corresponded with the composition-based model, since it presented lesser evaluation indices (RMSE, MAE, and R2) and more than 80% of residuals were within the established criteria (sum of the reproducibility and the uncertainty of the standard method). Although the routine-test-data-based method performed poorly according to the established criterion, its use could be recommended in cases of scarce data since it showed an acceptable value of R2 and physical consistency. Despite their empirical nature, the proposed prediction models based on MIR (mid-infrared) spectra, GC-MS, and routine test data had the potential to predict the RON and MON of real gasoline fuels commercialized in Colombia.