Identifying appropriate drilling sites is essential in hydrocarbon exploration and production, since precise site selection directly affects drilling success and economic efficiency. Seismic data capture and interpretation are fundamental to subsurface characterisation, offering geoscientists comprehensive insights into structural traps, stratigraphic features, and reservoir attributes. Contemporary innovations in seismic imaging such as three-dimensional (3D) and four-dimensional (4D) seismic surveys, seismic attribute analysis, and seismic inversion have markedly improved the capacity to mitigate exploration risk and refine well location. These methods provide accurate mapping of faults, anticlines, channel systems, and other geological characteristics indicative of hydrocarbon concentration. This research employs a qualitative review technique, using contemporary literature, industry reports, and empirical case studies to analyze the function of seismic data in finding potential drilling locations. The results demonstrate that the amalgamation of seismic interpretation with well-log data and petrophysical analysis enhances reservoir forecasting and reduces uncertainty in drilling choices. Moreover, new methodologies like artificial intelligence and machine learning in seismic interpretation have potential in automating attribute extraction and improving predictive modeling. The research affirms that seismic data is essential in contemporary hydrocarbon exploration and advocates for ongoing technology integration to enhance drilling precision, mitigate operational risk, and maximize resource extraction.