This research developed a robust Bayesian framework for evaluating geomaterials and soil-structure interactions involving deep excavation, utilizing Bayesian Regression and Bayesian Network methods that combined data training, validation, and updating into a cohesive framework. A methodology based on Bayesian Generalized Linear Model (a variant of Bayesian Regression) was applied to a laboratory-based case study on sustainable cementitious blends. The framework effectively used past research data to estimate design parameters even though the studies involved different mix design requirements. Then, a framework based on Gaussian Bayesian Network (a variant of Bayesian Network) incorporating Bayesian updating was applied to an established case history for deep excavation in clay. Two-dimensional finite element analysis was used to generate data for model training and updating. The GBN models accurately estimated responses such as wall deflections, wall bending moments, and ground surface settlements through a sequential updating process. The research was conducted using R programming language, with a custom R script designed to follow the process flow of the proposed framework. The successful application and validation of this framework demonstrated its potential for characterizing geomaterials and evaluating complex soil-structure interactions in deep excavation, leveraging on the availability of existing data and a reliable sequential updating process.