<p>This review presents the comprehensive approaches used for understanding the hydrological processes of groundwater/surface-water (GW-SW) interaction. Assessment of GW-SW interaction is crucial to enhance the understanding of water resource dynamics and optimizing sustainable water management strategies in diverse hydrological settings. GW-SW interaction can be assessed by employing various physically-based numerical models and soft computing techniques like machine learning (ML) and deep learning (DL). This comprehensive review aims to assess the diverse techniques employed for modelling GW-SW interaction and provide a deeper understanding of the methodologies. Through a systematic literature review and analysis, key methodologies are identified and scrutinized. The analysis encompasses the examination of research trends, geographic distribution, collaboration networks among researchers and citation patterns. Additionally, the validation of GW-SW assessment techniques are evaluated using statistical parameters such as <i>R</i>², root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE) and other metrics. As of April 2024, 107 papers published, indicating the rapid growth of the research area. An increasing publication trend has been observed over the past few years, with an annual growth rate of 9.7%. This review offers researchers and practitioners comprehensive insights into the advanced techniques for studying the methods used for assessing GW-SW interaction, facilitating better management of water resources against the complexities of climate change and socio-economic development.</p>