Automated seismic fault detection using artificial intelligence (AI) represents a transformative advance in subsurface interpretation, offering unprecedented precision and efficiency in identifying fault networks. Traditional manual interpretation of seismic volumes is time-consuming and subject to interpreter bias, often leading to inconsistent fault mapping. This paper reviews state-of-the-art AI methodologies—such as convolutional neural networks, deep learning architectures, and unsupervised feature extraction—for automated fault identification. We evaluate the performance of these models on diverse geological settings, highlighting their ability to detect subtle discontinuities, leverage transfer learning across basins, and integrate multi-attribute seismic data. Case studies demonstrate significant improvements in fault continuity, reduced false-positive rates, and accelerated interpretation workflows. Challenges—including training data scarcity, network generalization across varying seismic quality, and the need for explainable AI—are critically discussed. Finally, we outline best practices for integrating AI-driven fault detection into existing geoscience workflows, propose strategies for model validation and uncertainty quantification, and identify future research directions aimed at real-time monitoring and adaptive interpretation. The review underscores AI’s potential to revolutionize seismic fault mapping, improve reservoir characterization, and enhance decision-making in exploration and production.