The objective of this study is to develop a statistical model to accurately estimate the total base number (TBN) value of diesel engine oils on the basis of the Fourier transform infrared spectroscopy (FTIR) analysis. The research sample consisted of oils used in the course of 14,820 km. The samples were collected after each 1000 km and both FTIR and TBN measurements were performed. By applying the measured absorbance values, five statistical models aimed at predicting TBN values were elaborated with the use of the following information: aggregated values of measured absorbance in defined spectral ranges, extremes at wavenumbers, or the surface area of spectral bands related to the vibrations of specific molecular structures. The obtained models may be considered a continuation and an extension of previous studies of this type described in the literature on the subject. The results of the study and the analysis of the obtained data have led to the development of two models with high predictive capabilities (R² > 0.98, RMSE < 0.5). Another model, which had the smallest number of variables in comparison to other models, had markedly lower R² value (0.9496) and the highest RMSE (0.5596). Yet another model, where the dimensionality of the pre-processed full spectra was reduced to four aggregates through averaging, turned out to be slightly worse than the best one (R² = 0.9728). The study contributes to a more in-depth understanding of the FTIR-based TBN prediction tools that may be readily available to all interested parties.