Ground settlements above a tunnel as a result of tunnel construction can be predicted with the help of input variables that have direct physical significance. Several empirical and artificial intelligence methods for estimating ground settlements have been established by researchers. However, these methods have some limitations because the large number of influential factors involved makes tunnel-ground interaction complicated. In this work, a random forest (RF) was developed and employed to predict ground settlements above tunnels. To achieve this goal, tunnel geometry, geological properties, and construction parameters were investigated as input variables to utilize in the RF modeling, resulting in the maximum surface settlement value (Smax) and trough width (i) as the ground surface settlement index. To demonstrate the applicability of the RF model, two data sets associated with different features, which were obtained from a detailed investigation of different tunnel projects published in literature, were utilized for model development and were applied to check the performance capacity of the developed model. A fivefold cross-validation procedure was then applied to identify the optimal parameter values during modeling, and an external testing set was employed to validate the prediction performance of the model. Two performance measures, R2 and RMS error, were employed. The relative importance of different parameters in the prediction of ground settlements was also investigated. Findings demonstrate that the RF method provides promising results and offers an alternative means in predicting ground settlements induced by tunneling. [ABSTRACT FROM AUTHOR]