Machine learning (ML) has significantly advanced pore pressure prediction in complex geological settings, offering improved accuracy and reliability compared to traditional methods. Traditional techniques, such as empirical correlations and theoretical models, often struggle with heterogeneous and anisotropic formations, leading to uncertainties in pore pressure estimation. ML techniques, including neural networks, support vector machines, and ensemble learning, leverage large datasets and sophisticated algorithms to analyze geological complexities and predict pore pressure more effectively. These models can adapt to new data, improving their predictive capabilities over time. ML-driven approaches integrate diverse data sources, such as well logs, seismic data, and drilling parameters, to provide a holistic understanding of subsurface conditions. This integration enhances the accuracy of pressure forecasts, which is crucial for wellbore stability, drilling safety, and hydrocarbon recovery. Real-time data from drilling operations can be fed into ML models to dynamically update pore pressure estimates, allowing for immediate adjustments to drilling plans and reducing the risk of blowouts or other drilling hazards. ML techniques also identify subtle patterns and trends that traditional methods may miss, particularly in complex geological settings like deep-water environments, tectonically active regions, and unconventional reservoirs. Despite these advancements, challenges remain in the widespread adoption of ML-driven pore pressure prediction, including the need for extensive training datasets, model interpretability, and integration into existing geoscientific practices. Addressing these challenges requires interdisciplinary collaboration between geoscientists, data scientists, and engineers to develop robust, user-friendly ML solutions. Overall, ML-driven pore pressure prediction represents a significant advancement in managing subsurface geology, enhancing predictive accuracy and reliability, and improving safety, efficiency, and productivity in the oil and gas industry, particularly in challenging geological settings.Machine learning (ML) has significantly advanced pore pressure prediction in complex geological settings, offering improved accuracy and reliability compared to traditional methods. Traditional techniques, such as empirical correlations and theoretical models, often struggle with heterogeneous and anisotropic formations, leading to uncertainties in pore pressure estimation. ML techniques, including neural networks, support vector machines, and ensemble learning, leverage large datasets and sophisticated algorithms to analyze geological complexities and predict pore pressure more effectively. These models can adapt to new data, improving their predictive capabilities over time. ML-driven approaches integrate diverse data sources, such as well logs, seismic data, and drilling parameters, to provide a holistic understanding of subsurface conditions. This integration enhances the accuracy of pressure forecasts, which is crucial for wellbore stability, drilling safety, and hydrocarbon recovery. Real-time data from drilling operations can be fed into ML models to dynamically update pore pressure estimates, allowing for immediate adjustments to drilling plans and reducing the risk of blowouts or other drilling hazards. ML techniques also identify subtle patterns and trends that traditional methods may miss, particularly in complex geological settings like deep-water environments, tectonically active regions, and unconventional reservoirs. Despite these advancements, challenges remain in the widespread adoption of ML-driven pore pressure prediction, including the need for extensive training datasets, model interpretability, and integration into existing geoscientific practices. Addressing these challenges requires interdisciplinary collaboration between geoscientists, data scientists, and engineers to develop robust, user-friendly ML solutions. Overall, ML-driven pore pressure prediction represents a significant advancement in managing subsurface geology, enhancing predictive accuracy and reliability, and improving safety, efficiency, and productivity in the oil and gas industry, particularly in challenging geological settings.