The olive oil sector produces a substantial impact in the Mediterranean's economy and lifestyle. Many studies exist which try to optimize the different steps in the olive oil's production process. One of the main challenges for olive oil producers is the ability to asses and control the quality during the production cycle. For this purpose, several parameters need to be determined, such as the acidity, the UV absorption or the ethyl esters content. To achieve this, samples must be sent to an approved laboratory for chemical analysis. This approach is expensive and cannot be performed very frequently, making quality control of olive oil a real challenge. This work explores a new approach based on fluorescence spectroscopy and artificial intelligence (namely, 1-D convolutional neural networks) to predict the five chemical quality indicators of olive oil (acidity, peroxide value, UV spectroscopic parameters $K_{270}$ and $K_{232}$, and ethyl esters) from simple fluorescence spectra. Fluorescence spectroscopy is a very attractive optical technique since it does not require sample preparation, is non destructive, and, as shown in this work, can be easily implemented in small and cost-effective sensors. The results indicate that the proposed approach gives exceptional results in the quality determination and would make the continuous quality control of olive oil during and after the production process a reality. Additionally, this novel methodology presents potential applications as a support for quality specifications of olive oil, as defined by the European regulation.