Geostatistical concepts are essential for regional pore pressure mapping and prediction, offering advanced methodologies to address spatial variability and uncertainty in subsurface formations. This review explores the integration of geostatistical techniques to enhance the accuracy and reliability of pore pressure predictions over large geological regions. Accurate pore pressure prediction is critical in the oil and gas industry for optimizing drilling operations and ensuring wellbore stability. Traditional methods, limited by sparse data and simplified models, struggle to capture complex pore pressure patterns. Geostatistics provides a robust framework by leveraging spatial data analysis and probabilistic modeling. Key methods include kriging, co-kriging, and stochastic simulation, which create high-resolution pore pressure maps. Kriging predicts pore pressure at unsampled locations using spatial correlation, while co-kriging incorporates secondary variables like seismic attributes. Stochastic simulation generates multiple realizations to quantify uncertainty and assess drilling risks. Integration of seismic and well log data improves spatial resolution and reliability. Case studies demonstrate improved accuracy and reduced uncertainty in predictions, leading to better decision-making. Geostatistical methods enhance pore pressure mapping by integrating diverse data sources and spatial modeling, improving operational safety and efficiency. Challenges include data quality, computational complexity, and practical application. Future directions involve emerging trends, machine learning, and real-time monitoring to enhance prediction accuracy. Geostatistical approaches are vital for subsurface exploration, offering robust solutions for pore pressure prediction and risk management.Geostatistical concepts are essential for regional pore pressure mapping and prediction, offering advanced methodologies to address spatial variability and uncertainty in subsurface formations. This review explores the integration of geostatistical techniques to enhance the accuracy and reliability of pore pressure predictions over large geological regions. Accurate pore pressure prediction is critical in the oil and gas industry for optimizing drilling operations and ensuring wellbore stability. Traditional methods, limited by sparse data and simplified models, struggle to capture complex pore pressure patterns. Geostatistics provides a robust framework by leveraging spatial data analysis and probabilistic modeling. Key methods include kriging, co-kriging, and stochastic simulation, which create high-resolution pore pressure maps. Kriging predicts pore pressure at unsampled locations using spatial correlation, while co-kriging incorporates secondary variables like seismic attributes. Stochastic simulation generates multiple realizations to quantify uncertainty and assess drilling risks. Integration of seismic and well log data improves spatial resolution and reliability. Case studies demonstrate improved accuracy and reduced uncertainty in predictions, leading to better decision-making. Geostatistical methods enhance pore pressure mapping by integrating diverse data sources and spatial modeling, improving operational safety and efficiency. Challenges include data quality, computational complexity, and practical application. Future directions involve emerging trends, machine learning, and real-time monitoring to enhance prediction accuracy. Geostatistical approaches are vital for subsurface exploration, offering robust solutions for pore pressure prediction and risk management.