Chapter 2 has established the contextual and practical relevance of this research by examining global and sectoral challenges related to energy efficiency and sustainability, thereby fulfilling the requirement analysis stage of the relevance cycle in the three-cycle DSR model. Building on this foundation, this chapter addresses the grounding stage of the rigor cycle by reviewing the theoretical and methodological foundations for developing the proposed AI-driven decision-support framework. The chapter critically examines foundational theories, methodological developments, and state-of-the-art research on AI-enhanced decision support systems. It identifies key scientific principles and research gaps relevant to the design of a robust, interpretable, and human-centered framework for data-driven decision-making in complex industrial and energy systems. By synthesizing classical and emerging approaches, the review informs the methodological design and system architecture presented in subsequent chapters.