Cannabidiol (CBD, 1) is an active component of hemp oil and many other products that offers diverse health benefits. Near-infrared spectroscopy (NIRS) coupled with chemometrics was utilized to quantify the CBD (1) concentration in the hemp oil through the containing glass vial. NIRS provided a fast and cost-effective tool to measure chemical profiles for the hemp oil samples with various concentrations of CBD (1) and its acid precursor, i.e., cannabidiolic acid (CBDA, 2). The measured NIR spectra were transformed by using a Savitzky-Golay first-derivative filter to remove baseline drift. Two self-optimizing chemometric methods, super partial least-squares regression (sPLSR) and self-optimizing support vector elastic net (SOSVEN), were applied to construct automatically multivariate models that predict the concentrations of CBD (1) and total CBD (sum of 1 and 2 concentrations) of the hemp oil samples. The SOSVEN had validation errors of 6.4 mg/mL for the prediction of CBD (1) concentration and 6.6 mg/mL for the prediction of total CBD concentration, which are significantly lower than the errors given by sPLSR. Other than the lower validation errors, SOSVEN has another advantage over sPLSR in that it builds a multivariate model while selecting spectral features at the same time. These results demonstrated that NIR spectroscopy combined with chemometrics can be used as a rapid and cost-effective approach to determine the CBD (1) and total CBD concentrations in hemp oil. Manufacturers would benefit from the fast and reliable approach in quality assurance.