This chapter examines the transformative role of AI/ML-driven Quantitative Interpretation (QI) in seismic analysis, illustrating the shift from traditional methodologies to sophisticated AI integration. AI/ML techniques have enhanced the precision, speed, and scalability of seismic interpretation, particularly through advanced inversion methods such as Statistical AVO, Stochastic Inversion, and Bayesian Inversion. These innovations have significantly improved subsurface modeling and uncertainty quantification, allowing for more accurate reservoir characterization. The adoption of multi-scale, interdisciplinary frameworks further aids in reducing uncertainties, facilitating comprehensive subsurface models. Despite the challenges associated with AI/ML implementation—including the need for specialized expertise, high-quality data, and integration with established workflows—these technologies present a significant opportunity for optimizing exploration in complex geological settings. Ongoing advancements in data integration, deep learning, and autonomous systems will continue to refine seismic interpretation. This chapter emphasizes the critical potential of AI/ML to revolutionize the oil and gas industry, offering improved decision-making, enhanced resource management, and more efficient exploration and production workflows.