Oil and gas exploration and production struggles with meeting energy needs while minimizing environmental impact. Amidst artificial intelligence (AI) and machine learning (ML) to expedite energy exploration and minimize risks associated with energy investments through enhanced predictive accuracy. The application of AI/ML holds promise in expediting the identification of geological facies linked to reservoir rock and delineating seismic faults at seismic scale or generated 3D velocity models based on image and not forward modeling. However, before fully harnessing AI/ML capabilities, it's imperative to rigorously assess its ability to address these challenges with precision and confidence. This study aims to evaluate how machine learning can augment geoscience practices by enhancing accuracy, managing a multitude of 3D seismic attributes, and overcoming limitations in real-world interpretation. The work is divided into four chapters, each exploring different algorithms and methodologies. Chapter 2 uses generative adversarial networks (GANs) with geostatistical seismic inversion to characterize the Ray Reef gas storage area and generate alternative reservoir models. Chapter 3 compares unsupervised machine learning techniques with established geophysical methods for capturing reservoir facies and fluid effects using seismic attributes, validated on synthetic and field data. Chapter 4 employs unsupervised machine learning to classify lithologies and estimate fault probability in a large offshore seismic dataset, evaluating lateral seal risks. Finally, Chapter 5 delves into explainable machine learning (LIME) to understand uncertainties in 3D subsurface water saturation modeling, comparing random forest and recurrent neural network predictions. The combined findings of this research underscore the significant potential of machine learning techniques to enhance various aspects of oil and gas exploration and production workflows. By leveraging the power of generative adversarial networks, the study demonstrates their effectiveness in generating alternative reservoir models, providing valuable insights for reservoir characterization and risk assessment. Furthermore, the application of unsupervised learning algorithms showcases their capability in identifying reservoir facies and fluid effects from seismic data, offering a powerful tool for reducing interpretation uncertainties. The integration of explainable machine learning techniques, such as LIME, sheds light on the decision-making process of complex models, enabling a better understanding of subsurface water saturation modeling and associated uncertainties. Collectively, the results highlight the transformative impact of machine learning in geoscience, paving the way for more efficient and informed decision-making processes in oil and gas operations while mitigating environmental risks. Throughout the dissertation, the importance of integrating domain knowledge, rock physics principles, and synthetic data generation is emphasized to validate machine learning models and ensure physically meaningful results. The work highlights the potential and limitations of these techniques, advocating for a balanced approach that combines data-driven methods with domain understanding in geoscience applications.