The primary objective of this paper is to examine the many ways in which Artificial Intelligence (AI) and Machine Learning (ML) may be used in the field of Geotechnical Engineering. Researchers in the geotechnical engineering sector have been developing and using artificial intelligence (AI) and machine learning (ML) approaches for the last three decades. As a result of their efficacy in predicting complicated nonlinear interactions [1], these techniques have been widely used. Machine learning (ML) has recently piqued geotechnical engineers' attention due to its widespread usage in a variety of fields. Past reviewed studies have generally focused on machine learning methods; however, this work promotes an agenda that puts data at the center, develops unique techniques that are appropriate for geotechnical data (current and emerging), addresses the demands of present practice, exploits new possibilities from technological breakthroughs or meets emerging needs from information technology and takes use of existing knowledge and collected experience [1]. The three main components of this agenda—data centricity, fit for (and transformation of) practices, and geotechnical context—are together referred to as data-centric geotechnics. This" data first, experience core" goal will guide future geotechnical machine learning.