Quail eggs are one of the main natural sources of essential nutrients, presenting high amounts of protein, antioxidants, calcium, iron and phosphorus. However, its quality assessment demands laborious methods and chemicals, and there is currently no standard method do quantify its freshness. This work aimed to investigate the performance of a portable NIR spectrometer, in combination with machine learning, to estimate the freshness of quail eggs. Since there is no standard index to classify quail eggs, we compared Haugh Unit (HU), Yolk Index (YI) and the Egg Quality Index (EQI) as reference methods. Partial Least Squares Regression (PLSR) and Support Vector Machine Regression (SVMR) were used to build prediction models, and Partial Least Squares-Discriminant Analysis (PLSDA) and Support Vector Machine Classification (SVMC) for the development of classification models. For the first time, we demonstrated that EQI, which is a parameter that measures egg freshness according to the quality of the yolk and the albumen, is the best way to express the freshness of quail eggs. The best prediction models were obtained for YI and EQI, using SVMR, with RPD = 2.0–2.5 and RER >10, indicating good predictive capacity. PLSDA and SVMC models showed similar performance, correctly classifying more than 80% of the samples. The results obtained demonstrate the potential of portable NIR spectrometer for monitoring quail eggs freshness during storage.