Near-infrared (NIR) and mid-infrared (MIR) hyperspectral techniques in tandem with chemometric analyses were employed for developing multispectral real-time systems allowing the food industry to monitor moisture content (MC) in tubers including various potato and sweet potato products during drying. Multivariate models were established by partial least-squares regression (PLSR), support vector machine regression (SVMR), locally weighted partial least square regression (LWPLSR), and back propagation artificial neural network (BPANN) using full spectral ranges of 10372–6105 cm−1 (Spectral Set I), 3996–600 cm−1 (Spectral Set II), and 1700–900 cm−1 (Spectral Set III). The LWPLSR from Spectral Set I and BPANN from Spectral Set II and III, obtained the highest accuracies for tuber MC prediction. Then, both regression coefficient (RC) and successive projection algorithm (SPA) were respectively used for the selection of feature wavelengths in Spectral Set I, II and III. Instead of choosing many groups of characteristic variables for different varieties of potatoes and sweet potatoes, one set of feature variables for all tubers was selected from each spectral region for the convenience of industrial application. Eventually, six sets of feature wavelengths chosen from Spectral Set I, II and III were used to optimize models. The simplified SPA-LWPLSR from Spectral Set II and SPA-BPANN from Spectral Set III acquired good model performances for the tuber MC prediction, with determination coefficients in prediction (R2P) of 0.950 and 0.904, respectively. The RC-BPANN model from Spectral Set I achieved the highest R2P of 0.965. Such accuracies were comparable to that of full spectral models. The results reveal that hyperspectral techniques have great potential in the food industry for real-time measurement of tuber MC.