Pillars are important structural elements to provide temporary or permanent support in underground spaces. Unstable pillars can result in rock sloughing, leading to roof collapse, and they can also cause rock burst. Hence, prediction of underground pillar stability is important. This paper presents a novel application of Logistic Model Trees (LMT) to predict underground pillar stability. Seven parameters —pillar width, pillar height, ratio of pillar width to height, uniaxial compressive strength of rock, average pillar stress, underground depth, and Bord width— are employed to construct LMTs for rock and coal pillars. The LogitBoost algorithm is applied to train two data sets of rock and coal pillars case histories. The two models are validated with (i) 10-fold cross-validation and with (ii) another set of new case histories. Results suggest that the accuracy of the proposed LMT is the highest among other common machine learning methods previously employed in the literature. Moreover, a sensitivity analysis indicates that the average stress, p, and the ratio of pillar width to height, r, are the most influential parameters for the proposed models.