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Abstract For decades, age‐structured stock assessments have been a key component to managing fishery resources worldwide. Fisheries management systems have been under increasing demand to generate a greater volume and quality of age estimates. Traditional aging techniques, which require physical preparation followed by microscopic examination of fish otoliths, are labor‐intensive, expensive, and inherently subjective among individual analysts, making repeatability and precision a challenge. Here we investigated an innovative approach to aging fish from their otoliths using Fourier‐transformed near‐infrared spectroscopy and partial least squares regression models. Models were fit to and validated on spectra and used to microscopically estimate ages of Pacific cod from three years of fishery‐independent otolith data out of the Bering sea. Calibrated and validated models for each year, as well as on an ensemble of the three years, yielded high precision for the multiyear model (R2 = 0.869, RMSE = 0.614, PA = 63%, CV = 7.412), and independent year models (R2 = 0.844–0.891, RMSE = 0.555–0.615, PA = 65%, CV = 6.313–6.775). These metrics of model performance were highly comparable to precision from the traditional microscopic aging approach (R2 = 0.763–0.869, RMSE = 0.639–0.737, PA = 63%–70%, CV = 5.671–6.698). In all cases, a two‐sided Kolmogorov–Smirnov test showed no significant difference between reference and model estimated age distributions. Our results illustrate how Fourier‐transformed near‐infrared spectroscopy can be utilized on otoliths to predict age estimates with substantially greater efficiency, good precision, high repeatability, and no loss in data integrity compared to the traditional microscopic method for aging Pacific cod.