Pitaya (Hylocereus polyrhizus), also known as dragon fruit, is an exotic and highly valued fruit with a high amount of fiber and vitamins, and its quality is often related to attributes such as soluble solids, moisture content, and acidity. Traditional analytical techniques (e.g., gas chromatograph-mass spectrometry – GC–MS) for physicochemical quantification are costly and not environmentally friendly. This work proposes a quick and non-destructive evaluation of pitaya quality using low-cost near-infrared spectroscopy (NIRS) and electronic nose (e-nose) devices. Classification models for either NIR spectra or e-nose data as predictors presented accuracy higher than 90% when classifying samples according to their shelf-life index stage (SLI30, 50, 80, and 100). Total titratable acidity (TA) and pH could be predicted using partial least squares regression (PLSR) and NIR spectra as predictors with coefficients of determination (R2P) of 0.89 and 0.83, respectively, and root means square error (RMSEP) of 0.03 and 0.23, respectively. Similarly, PLSR models for the prediction of TA and pH using e-nose data achieved R2P of 0.85 and 0.86, and RMSEP of 0.04 and 0.22 respectively. RPD and RER values for NIRS show that all predictors can be used to at least distinguish between low and high values. The results demonstrate that inexpensive devices based on NIRS, and a novel low-cost e-nose could be used in combination for the prediction of TSS, pH, TA, moisture, and phenolics, as well as to classify pitaya according to their shelf-life stages.