Accurate subsurface characterization remains a fundamental challenge across the energy industry, where traditional interpretation methods often fail to capture critical reservoir details needed for both conventional and emerging energy solutions. This presentation showcases how advanced seismic attributes combined with machine learning techniques can significantly enhance reservoir characterization while reducing subsurface uncertainty and improving project risk assessment. Based on research conducted with the Attribute Assisted Seismic Processing and Interpretation (AASPI) consortium at the University of Oklahoma, I present integrated workflows that demonstrably improve seismic facies classification and fault detection accuracy. Using case studies in a variety of geologic settings, I illustrate how these methods provide valuable insights applicable not only to conventional hydrocarbon systems but also to sustainable energy initiatives including carbon storage and geothermal development. Attendees will gain practical understanding of strategic attribute selection and effective machine learning approaches that reveal subtle subsurface features essential for assessing reservoir quality and mitigating subsurface risk. These techniques empower geoscientists to contribute more effectively to reservoir characterization across the spectrum of energy applications, bridging traditional expertise with emerging sustainability goals.