Cerek, KacperKacperCerekDao, Duy AnhDuy AnhDaoHadjiloo, ElnazElnazHadjilooGrabe, JürgenJürgenGrabe2026-10-072026-10-072026-06-08Machine Learning and Data Science in Geotechnics 2 (1): 135–152 (2026)https://hdl.handle.net/11420/65233Purpose -This state-of-the-art paper provides an overview of surrogate modelling methods suitable for geotechnical engineering, covering the entire cycle of model development. Design/methodology/approach -The paper discusses neural network-based surrogate modelling and its applicability in geotechnical engineering, including its limitations. It addresses techniques for data set generation, sampling, tuning, selecting activation functions and assessing accuracy Findings - Each step during the development of surrogate models offers various options and therefore requires a systematic approach. Besides standardised benchmarking and richer data sets key areas for future research include advanced or hybrid architectures, such as physics-informed long short-term memory networks and transformers to improve generalisability, efficiency and physical consistency.</jats:p> Originality/value -This paper reviews and highlights the most promising methods for surrogate model development in geotechnical engineering, considering the most critical aspects for building high-performing predictive models and lays an outlook for future research.en3029-0414Machine learning and data science in geotechnics20261135152Emeraldhttps://creativecommons.org/licenses/by/4.0/Artificial intelligenceNeural networksSurrogate modellingComputational geotechnicsSustainable developmentTechnology::624: Civil Engineering, Environmental Engineering::624.1: Structural EngineeringComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial IntelligenceDevelopment of neural network-based surrogate models in geotechnics: a state-of- the-art reviewJournal Article10.1108/mlag-04-2025-001110.15480/882.18800