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Artificial intelligence (AI) tools increasingly enhance the efficiency and consistency of seismic interpretation, particularly in structurally complex areas or areas where data quality is reduced by acquisition limitations. As a result, interpretations can become difficult and time-consuming, especially in the context of structural interpretation and fault tracking. To evaluate the performance of AI-based fault detection, we applied Geoplat AI software to a 3D seismic volume from the Drmno Basin, located at the southeastern margin of the Pannonian SuperBasin in Serbia.A conventional structural interpretation was first performed by mapping the major fault systems, then minor fault systems, generating fault sticks and polygons for all visible faults and developing a structural model to illustrate the basin's opening and evolution. Subsequently, AI-based workflows were applied in order to enhance the quality of the seismic data. This involved removing noise, restoring reflections, highlighting fault zones, and applying smoothing filters. The final step was the utilization of a fault tracking tool that segments the seismic data, recognizes fault zones, traces them, identifies structural patterns, and calculates a probability field. The AI-derived fault interpretation was then compared with the manual interpretation.The results indicate that the Drmno basin was developed under an extensional tectonic regime during the Early Miocene, which formed a large Morava detachment fault and opened accommodation of the basin. The basin itself has complex architecture in the syn-rift phase, with many synthetic and few antithetic faults, oriented from the east to the west. During the stage of the rift climax, the dominant fault systems remained consistent, with most syn-rift structures continuing to accommodate the subsidence formed by the Morava detachment. The shift in the tectonic conditions in the post-rift stage leads to the formation of systems of parallel faults in the younger sediments, adjusting strike-slip movements in a compressional tectonic field. The younger structures are dominantly oriented in the north-south direction, or reactivated older fault structures.The AI tool effectively interpreted fault systems in the younger geological units, benefiting from higher data quality, and clearly indicated younger fault systems with a high level of certainty. However, in the lower part of the seismic cube, the basement structures remain unclear or unrecognized. Reactivated fault surfaces and a significant fault zone are evident in the interpretation. In areas with low-quality seismic data, the AI tool struggled to trace faults accurately, resulting in geologically inconsistent fault patterns.Overall, the AI-based 3D fault tracking tool proved effective in resolving the main structural framework of the basin. The dominant fault directions are clearly identifiable, and the main geological structures have been mapped with reasonable precision. The AI-supported interpretation successfully captures the main structural trends and provides a solid basis for evaluating the tectonic evolution. This case study demonstrates the potential of AI to support structural interpretation and tectonic analysis of complex sedimentary basins.