Abstarct: Salt domes, as complex geological structures, create numerous interpretation challenges in seismic data due to their chaotic reflection patterns. Furthermore, the large volume of 3D seismic data and the time-consuming nature of manual interpretation have made the development of automated methods an essential necessity. This research aims to propose an automated hybrid method for accurately identifying salt dome boundaries and generating a 3D model of these structures. To achieve this, a three-stage algorithm was designed and implemented. In the first stage, three seismic attributes—energy, entropy, and chaos—were extracted as the most suitable indicators for detecting salt dome texture from 3D seismic data of the Strait of Hormuz region. In the second stage, the output of these attributes was converted into binary images and refined by applying an optimized sequence of mathematical morphology operators, including opening and hole filling. In the third stage, the refined images were used with the Canny edge detection algorithm to extract the final salt dome boundaries and generate its 3D model. The results from applying the algorithm to field data demonstrated that the proposed method successfully revealed salt dome boundaries with high clarity and continuity. Comparison with expert manual interpretation showed significant agreement between the extracted boundaries and the actual boundaries. Moreover, the entire processing stage after attribute computation was completed in approximately 30 seconds, indicating the computational efficiency of the method for processing large-scale seismic data. In summary, this research demonstrates that the systematic integration of seismic attributes with mathematical morphology can serve as an automated, accurate, and efficient frxamework for identifying salt domes in 3D seismic data, significantly reducing the interpretation process's reliance on the interpreter's subjective judgment.