Geotechnical engineering deals with materials (e.g., soil and rock) that, by their very nature, exhibit varied and behavior due to the physical processes associated with the formation of these materials. Modeling such materials’ behavior is complicated and usually beyond the ability of most traditional forms of physically-based engineering methods. Artificial intelligence (AI) is becoming more popular and particularly amenable to modeling most geotechnical engineering materials’ complex behavior because it has demonstrated superior predictive ability compared to traditional methods. Over the last decade, AI has been applied successfully to virtually every problem in geotechnical engineering. However, despite this success, AI techniques are still facing classical opposition due to some inherent reasons such as lack of transparency, knowledge extraction, and model uncertainty, which will discuss in detail in this chapter. Among the available AI, techniques are artificial neural networks (ANNs), genetic programming (GP), evolutionary polynomial regression (EPR), support vector machines, M5 model trees, and K-nearest neighbors (Elshorbagy et al.,2010). This chapter will focus on three AI techniques, including ANNs, GP, and EPR.