Data collected for offshore oil and gas developments is increasingly digitial and technology has advanced in recent years but data integration for site characterization remains mostly in analog form and quantitative. A framework quantitatively integrating geophysical and geotechnical data is presented with the intent of extracting soil properties directly from geophysical data for planning, front-end and detailed design purposes. The framework comprises acoustic impedance inversion using high-resolution seismic data, correlating the acoustic impedance with the soil porosity and using a machine learning algorithm trained with a regional geotechnical database to predict the soil strength. Uncertainty is reduced by selective ground-truthing of geophysical profiles of interest with geotechnical investigations that are less dependent upon specific layouts and more focused on the prevailing geology of the site. The approach maximizes the use of available data, enables more informed decisions on the value of additional data collection and reduces cost, schedule and exposure with optimal field operations for site characterization.