The contribution and impact of combined laser ablation inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOF-MS) and laser-induced breakdown spectroscopy (LIBS) were evaluated for the discrimination analysis of different coal samples. This tandem approach allows simultaneous determination of major and minor elements (C, H, Si, Ca, Al, Mg, etc.) and trace elements (V, Ba, Pb, U, etc.) in the coal. The research focused on coal-classification strategies based on principle component analysis (PCA) combined with K-means clustering, partial least-squares discrimination analysis (PLS-DA), and support vector machine (SVM) for analytical performance. Correlation analyses performed from TOF mass and LIBS emission spectra from the coal samples showed that most major, minor, and trace element emissions had negative correlation with the volatile content. Suitable variables for the classification models were determined from these data. The individual TOF data, LIBS data, and combined data of TOF and LIBS as the inputs for different models were analyzed and compared. In all cases, the results obtained with the combined TOF and LIBS data were found to be superior to those obtained with the individual TOF or LIBS data. The nonlinear SVM model combined with TOF and LIBS data provided the best coal-classification performance, with a classification accuracy of up to 98%.