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This paper addresses Bayesian-based data-driven site characterization methods for estimating soil parameters used in top-hole casing design. Different models are applied to datasets of piezocone tests (CPTu) conducted in Brazilian fields and their performance is compared to some characterization techniques currently employed by the oil and gas industry. Regression models are crucial for soil characterization for top-hole casing design. Data-driven methods consist of a powerful tool for this purpose, allowing handling uncertainties originating from soil variability. This paper addresses machine learning models devoted to sparse data, namely Geotechnical lasso (Glasso) and Gaussian Process Regression (GPR) from a Bayesian perspective considering prior knowledge of site information. This approach provides statistical information on soil parameters like undrained shear strength, supporting structural …