High-value hydrocarbon developments are inherently exposed to complex subsurface uncertainties that significantly influence project economics, operational safety, and long-term asset performance. These uncertainties arise from incomplete geological characterization, reservoir heterogeneity, pressure regimes, fluid behavior, and geomechanical interactions, which collectively introduce risks across exploration, appraisal, and production phases. This paper presents a comprehensive review of a multi-layer subsurface risk quantification and mitigation framework designed to systematically integrate geological, geophysical, petrophysical, and engineering data into a unified decision-support structure. The study synthesizes advances in reservoir modeling, uncertainty quantification, probabilistic risk assessment, and digital subsurface analytics to identify critical methodologies for evaluating and managing subsurface risks in high-value hydrocarbon projects. Central to the proposed framework is a layered architecture that captures risks at different subsurface levels, including structural, stratigraphic, reservoir, and dynamic production domains. The framework incorporates advanced techniques such as stochastic modeling, Monte Carlo simulation, machine learning-driven prediction, and real-time data assimilation to quantify uncertainty and optimize decision-making under risk. Furthermore, it emphasizes the integration of risk mitigation strategies, including adaptive drilling programs, enhanced reservoir monitoring, and dynamic production optimization, to reduce exposure to operational and financial uncertainties. The study also explores the role of digital twin technologies and predictive analytics in enabling continuous risk monitoring and proactive mitigation throughout the asset lifecycle. Key findings indicate that multi-layer risk integration significantly improves decision accuracy, reduces non-productive time, and enhances recovery efficiency in complex reservoirs. The proposed framework contributes to the advancement of subsurface risk management by providing a structured, scalable, and data-driven approach for optimizing hydrocarbon development strategies. It offers valuable insights for industry practitioners and establishes a foundation for future research in integrated subsurface analytics and risk-informed decision-making.