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Geotechnical site characterization is a crucial factor in the effective planning, design, and implementation of civil engineering projects. In the evolving landscape of infrastructure development, the integration of advanced technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) has emerged as a transformative strategy to improve the precision and efficiency of geotechnical site characterization processes. This article delves into the combined application of AI and IoT in geotechnical site characterization, encompassing a diverse range of technologies, models, tools, and frameworks. AI, utilizing its machine learning algorithms, has the capacity to analyse extensive geospatial and geological data, facilitating more accurate identification of subsurface conditions. Neural networks and deep learning models play a role in examining geological features, predicting soil behaviour, and evaluating potential risks associated with construction projects. In conjunction with AI, the incorporation of IoT technologies enables real-time monitoring and data acquisition at geotechnical sites. Ground-embedded sensor networks gather geophysical data, including soil moisture, temperature, and pressure, providing a dynamic and continuous understanding of subsurface conditions. This real-time data feeds into AI models, creating a feedback loop that refines predictions and enhances the precision of site characterization. Moreover, the article introduces various tools and frameworks that facilitate the seamless integration of AI and IoT in geotechnical engineering. Geographic Information Systems (GIS) are employed for spatial analysis, aiding in the …