Natural foods and food-related antioxidants such as phenolic phytochemicals are of great interest due to their preventive properties against oxidative damage. Olive tree leaves contain high quality and amount of phenolic compounds including oleuropein and therefore considered as nutraceutically valuable materials. The composition of olive leaves, its phenolics and antioxidant power are influenced by numerous factors causing great variation among samples. Additionally, traditional analytical methods performed to quantify these parameters in each product entail long and complicated sample preparation procedures, the use of toxic chemicals, skilled labors, instrumentation and sophisticated laboratory conditions. One appealing alternative is the use of infrared spectroscopy since it gives information about the food composition quickly and it is a multi-parametric and environmentally friendly choice. Therefore, we investigated the oleuropein, total phenolic content, total flavonoid content and antioxidant activity levels of 23 common cultivars of olive leaves harvested from Turkey and Italy using traditional reference methods and also developed near and mid-infrared based partial least squares regression (PLSR) models to predict these parameters without the need of sample preparation. Internal validations of the PLSR calibration models were done using full cross-validation and yielded very high correlation coefficients (0.95) and low errors in predictions (% standard error of cross-validation for parameters were lower than 7.54%). The levels of all the parameters of interest could be successfully predicted using both NIR and MIR instrumentation within seconds. Overall, infrared spectroscopy along with chemometrics exhibited great potential for future olive leave studies.