Table olives are among the most widely consumed fermented foods and constitute an essential part of the Mediterranean diet. Nevertheless, their production is still based on empirical and craft-based processing practices. As a result, significant challenges must be faced regarding product safety and quality, since the final products often exhibit variability and instability. The aim of this study was to evaluate the efficacy of UV–Vis spectroscopy as a rapid, real-time, and non-invasive tool for monitoring biochemical changes in brine during the fermentation of cv. Kalamata natural black table olives. For this purpose, olives were processed according to industrial practice in 7% (w/v) NaCl brine for 145 days. Brine samples were routinely collected and UV–Vis spectra were collected in the range 330-800 nm. The acquired spectral data were initially subjected to principal components analysis for dimensionality reduction. The scores of the first four principal components explaining 99% of the variability of the original dataset were further used as input variables for an artificial neural network (ANN) model to correlate spectral information with fermentation time. The predictive performance of the ANN was further compared with a Partial Least Squares Regression (PLS-R) model developed on the same dataset, composed of training and validation subsets. Results demonstrated that the ANN model achieved high predictive accuracy reflected by relatively high values of R2 (0.94) and low values of RMSE (9.0 days) computed for validation subset. These values were more satisfactory than those obtained by the PLS-R model that was used as reference approach (R2 = 0.88 and RMSE = 12.5 days) for the validation subset. Overall, the findings of the study provided promising perspectives for the use of UV–Vis spectroscopy combined with chemometric modeling, as a rapid and non-invasive analytical approach to monitor the fermentation of table olives.