Researchers analyzing data collected from borehole drilling projects can face dozens of terabytes of seismic, hydrologic, geologic, and rock mechanics data, including complex imagery, physical measurements, and expert-written reports. These diverse data sets play a pivotal role in understanding solid Earth processes. Ingesting and analyzing such data presents a colossal challenge that typically demands a team of experts and large amounts of time. The utilization of Artificial Intelligence (AI) and machine learning emerges as a compelling approach to help tackle the volume and complexity of drilling data. This paper presents an AI-based pipeline for ingesting data from the Oman Drilling Project’s Multi-borehole Observatory. The study focuses on the alteration of peridotite core segments taken from Borehole BA1B, utilizing a catboost classification model trained on an integrated data set of machine learning segmented core images, physical measurements, geological, lithographic data, and AI-summarized expert texts and feature selection. This paper’s central objective is to establish a repeatable, efficient pattern for processing such multifaceted borehole data through connecting fracture networks detected in the borehole BA1B imagery to the host rock alteration.