This paper develops and employs an ensemble machine learning (ML) model for prediction of surface chloride concentration (Cs) of concrete, which is an essential parameter for durability design and service life prediction of concrete structures in marine environment. For this purpose, a database containing 642 data-records of field exposure data of Cs (along with the associated mixture proportion parameters, environmental conditions and exposure time) is established based on extensive literature surveying, which covers splash, tidal, and submerged zones in various areas in the world. The database is used to train five standalone ML models, that is, linear regression (LR), Gaussian process regression (GPR), support vector machine (SVM), multilayer perceptron artificial neural network (MLP-ANN) and random forests (RF) models, as well as an ensemble weighted voting-based ML model, and subsequently used to compare their prediction performances. It is shown that, by meta-heuristically combining predictions of RF, MLP-ANN, and SVM, the ensemble ML model produces higher accuracy of prediction compared to all standalone ML models tested in this study. The prediction performances of eight conventional quantitative models for Cs prediction are also analyzed based on the testing dataset selected for ML. The results show that adoption of more diverse datasets and consideration of more factors in conventional models can improve their prediction performance. The ensemble ML model established on a large database, can easily consider the twelve influencing factors (which is difficult for conventional models) in the database, and has superior prediction performance, yet better time-efficiency, compared to conventional models.