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Abstract Drilling operations in the Gulf of Thailand's mature brown fields are increasingly challenged by reservoir depletion and highly uncertain formation pressures, where conventional downhole tools often fail to provide reliable pore pressure data during severe loss circulation events. These gaps in information can lead to operational risks, inefficient decision-making, and suboptimal perforation strategies. To address these issues, a machine learning-based pore pressure prediction model was developed to deliver real-time estimates, improve drilling performance, and maximize hydrocarbon recovery. Since its inception in 2018 as a basic classification tool, the model has undergone significant evolution into a robust regression-based system. Early limitations in accuracy and input parameters were progressively overcome through enhancements such as polynomial regression in 2019 and the integration of XGBoost, achieving ±1.5 ppg accuracy. By 2023, the model incorporated rigorous validation by technical subject matter experts, optimized code for automation, and real-time cloud-based deployment, enabling predictions to be visualized through Power BI for centralized interpretation by Earth Scientists and Petroleum Engineers. The model now supports three critical applications: drilling performance optimization through continuous updates to fracture pressure estimates, reducing lost circulation incidents and improving efficiency in depleted zones; formation test decision support by validating pressure conditions when geological indicators suggest anomalies, ensuring accurate reserve estimation; and perforation strategy by predicting pressures across all sand intervals, including legacy wells, identifying overlooked hydrocarbon zones and enabling final perforation opportunities prior to abandonment. This study demonstrates how machine learning can transform existing reservoir properties and drilling parameters into actionable insights, integrating predictive analytics with real-time operations to enhance drilling safety, reduce non-productive time, and deliver significant cost savings. By bridging data gaps and enabling proactive decision-making, this approach represents a novel application of advanced analytics in gas field development, offering a scalable solution for optimizing drilling and completion strategies in complex, depleted reservoirs.