Artificial intelligence (AI) has been gaining momentum to develop advanced models that can predict complex geotechnical properties. In this paper, a state-of-the-art review has been conducted of studies that utilize machine learning algorithms for solving geotechnical engineering problems. The study indicates that the major areas of AI application in geotechnical engineering have been material characterization, foundation engineering, tunneling, and slope stability. Based on the strength of the dataset used for development of the models, studies have been classified to have small, medium, and large dataset. The median value of the dataset has been found to be 199, with the range of values to be varying between 58,785 and 23. Deep learning has been used widely for the development of the data-driven models. Other methods that have been used are tree-based regression techniques (RF, XGBoost, DTR), SVR, and MARS. The application of AI in geotechnical engineering has observed exponential growth over the last decade. The review discusses the possible areas of exploration and challenges of usage of AI techniques in geotechnical engineering.