Liver cancer has high mortality and low early detection rates. To enable timely and accurate auxiliary diagnosis of liver cancer, this study uses laser-induced breakdown spectroscopy (LIBS) combined with machine learning to perform elemental imaging and classification of liver tumor tissue and adjacent muscle tissue in a nude mouse. First, sample slices of liver tumor and adjacent muscle tissues of a nude mouse were prepared, and characteristic element spectra and spatial distribution maps were obtained using an LIBS system. Second, 400 spectral data points from the two tissue types were preprocessed and randomly divided into training, validation, and test sets. Support vector machine, Fisher discriminant analysis, kernel extreme learning machine (KELM), random forest (RF), and Bayes classification algorithms were used to develop a discriminant model for the full spectrum data of the sample slices for the classification. Finally, principal component analysis (PCA) was applied for feature extraction. The results showed differences in element content between the LIBS spectra and the Ca, K, and Mg distribution maps of liver tumor and adjacent muscle tissues. Further the classification of sample slices using the aforementioned algorithms revealed that full-spectrum classification accuracy exceeded 95%; however, modeling and classification required considerable time. Selecting eight principal components with a cumulative contribution rate of 95% from PCA as input data can considerably reduce the modeling and classification time while maintaining accuracy. Compared with full-spectrum RF and KELM, the classification time using PCA-RF was reduced to 0.13 s, demonstrating a substantial optimization effect. PCA-KELM achieved the best overall modeling and classification performance, reducing the modeling time to 0.04 s. These findings indicate that, when combined with appropriate machine learning algorithms, LIBS can effectively differentiate liver tumors from adjacent muscle tissue, which could be valuable for clinical applications in the future.