In the present research, storage conditions of extra virgin olive oil (EVOO) have been monitored using cost-effective fluorescence sensors integrated with intelligent algorithms. Three different Spanish pure EVOOs (Arbequina, Cornicabra, and Picual varieties), as well as samples adulterated with expired EVOO, were kept under different environmental conditions: presence or absence of light, bottle material (plastic or glass), and clearness of glass (uncolored or brown glass). To evaluate the effect of these conditions, 54 samples were prepared, and their emission spectra were measured 10 times during a 58-day period. The most statistically relevant information from these spectra was located by the relief-F feature selection method, which led to the design of machine learning-based models. With this aim, up to 158,250 artificial neural network-based models were designed and tested. It was possible to distinguish different storage conditions in terms of light exposure, bottle material, and clearness, as well as locate adulterations. All of the trained and optimized classifiers for all three EVOO varietals reached accuracies ranging between 91 and 100% after meticulous validations. The used technique is fast, portable, user-friendly, inexpensive, and non-destructive, and, therefore, it could be of great use for the olive oil sector as it enables real-time quality control and fraud detection. •Evaluation of the effect of the container, light, and adulterations on EVOO.•Fluorescence spectroscopy to locate changes in EVOO due to environment.•Neural networks to classify different storage/distribution conditions of EVOO.•Accurate, fast, cost-effective, and real-time approach to aid the olive oil sector.•Chemometric tool potentially applicable to other foods and for new purposes.