Accurate pigment classification is essential for the analysis and conservation of historical paintings. This study presents a non-invasive approach based on supervised machine learning for classifying pigments using image data acquired under three distinct spectral illumination conditions: visible-light reflectography (VIS), ultraviolet false-color imaging (UVFC), and infrared false-color imaging (IRFC). A dataset was constructed by extracting 32 × 32 pixel patches from pigment samples, resulting in 200 classes representing 40 unique pigments with five preparation variants each. A total of 600 initial raw images were acquired, from which 4000 image patches were extracted for feature engineering. Feature vectors were obtained from visible reflectography, ultraviolet false-color imaging (UVFC), and infrared false-color imaging (IRFC) using statistical descriptors derived from RGB channels. This study demonstrates that accurate pigment classification can be achieved with a minimal set of three illumination types, offering a practical and cost-effective alternative to hyperspectral imaging in real-world conservation practice. Among the evaluated classifiers, the random forest model achieved the highest accuracy (99.30 $\pm 0.15$%). The trained model was subsequently validated on annotated regions of historical paintings, demonstrating its robustness and applicability. The proposed framework offers a lightweight, interpretable, and scalable solution for non-invasive pigment analysis in cultural heritage research that can be implemented with accessible imaging hardware and minimal post-processing.