Seismic data processing underpins critical decision-making in hydrocarbon exploration and reservoir development. However, the integrity and quality of raw seismic datasets can significantly influence processing outcomes, from noise attenuation and velocity analysis to migration and inversion. This review synthesizes current methodologies for assessing and ensuring seismic data quality, examining how common data integrity issues—such as acquisition gaps, timing errors, amplitude inconsistencies, and poor sensor calibration—propagate through processing workflows. We evaluate the impacts of suboptimal data on noise suppression, resolution, and final structural and stratigraphic interpretations, highlighting the risks of mispositioned horizons, blurred fault imaging, and erroneous attribute extraction. Drawing on peer-reviewed studies and industry case examples, the paper demonstrates that rigorous quality control (QC) at each processing stage maximizes signal fidelity and reduces uncertainty in reservoir characterization. Finally, we propose a framework of best practices—including real-time QC during acquisition, automated integrity checks, and integrated feedback loops between processing and interpretation teams—to enhance overall data reliability. By linking data quality metrics directly to economic and operational outcomes, this review underscores the essential role of robust data integrity in optimizing exploration success and development planning.