Abstract This thesis embarks on a comprehensive exploration of seismic data analysis, focusing on two critical facets: salt dome detection and seismic image enhancement. The study integrates unsupervised machine learning, particularly K-means clustering, with advanced deep learning techniques, specifically employing the Super-Resolution Generative Adversarial Network (SRGAN) model. In the pursuit of salt dome detection, we employ K-means clustering in tandem with the OpenCV library. This powerful combination enables the precise demarcation of salt dome boundaries within seismic images, thereby advancing the accuracy of geological interpretations and subsurface structure assessments. Concurrently, our application of the SRGAN model has brought about a significant enhancement in the resolution of seismic images.