Power transformers are essential components in electrical energy distribution. One of their most important parts is the insulation system, consisting of Kraft paper immersed in insulating oil. Interfacial tension and color are major parameters used for assessing oil quality and the system׳s degradation. This work proposes the use of near infrared (NIR), molecular fluorescence, and ¹H nuclear magnetic resonance (NMR) spectroscopy methods combined with chemometric multivariate calibration methods (Partial Least Squares – PLS) to predict interfacial tension and color in insulating mineral oil samples. Interfacial tension and color were also determined using tensiometry and colorimetry as standard reference methods, respectively. The best PLS model was obtained when NIR, fluorescence, and NMR data were combined (data fusion), demonstrating synergy among them. An optimal PLS model was calculated using the selected group of variables according to their importance on PLS projections (VIP). The root mean square errors of prediction (RMSEP) values of 2.9 mN m⁻¹ and 0.3 were estimated for interfacial tension and color, respectively. Mean relative standard deviations of 1.5% for interfacial tension and 6% for color were registered, meeting quality control requirements set by electrical energy companies. The methods proposed in this work are rapid and simple, showing great advantages over traditional approaches, which are slow and environmentally unfriendly due to chemical waste generation.