In recent years, the integration of Artificial Intelligence (AI) and Principal Component Analysis (PCA) has significantly transformed drilling engineering, driving notable advancements in both the efficiency and accuracy of subsurface exploration and production. The fusion of these technologies offers a powerful approach to managing and interpreting the vast, complex datasets typically associated with drilling operations. This research looks into the application of AI techniques in conjunction with PCA to analyse well logs, reservoir data, and production metrics, aiming to uncover critical patterns and insights that traditional methods might overlook. By utilizing AI algorithms, particularly machine learning models, this study harnesses the ability of AI to process and learn from large volumes of data, making it possible to predict and optimize drilling outcomes with greater precision. PCA, as a dimensionality reduction technique, plays a crucial role by simplifying these complex datasets, enabling more efficient data processing and enhancing the interpretability of results. The combination of AI and PCA not only streamlines the analysis but also facilitates the identification of key variables and trends that influence drilling performance. Ultimately, this research contributes to the development of more intelligent and data-driven approaches in drilling engineering, promising to optimize operations and reduce risks in subsurface exploration.