Building energy efficiency has emerged as one of the most critical levers for achieving global climate mitigation, sustainable development, and economic resilience objectives. Buildings account for a substantial share of global energy consumption and greenhouse gas emissions, making them a focal point for technological innovation, policy intervention, and financial analysis. Recent advances in machine learning, reinforcement learning, building information modeling, and lifecycle optimization have significantly expanded the methodological toolkit available for improving building energy performance across design, operation, and retrofit stages. At the same time, economic theories of uncertainty, risk, and information asymmetry, alongside evolving regulatory standards and energy efficiency policies, shape the feasibility and adoption of these technologies. This article presents a comprehensive, publication-ready synthesis of contemporary research on building energy efficiency, drawing strictly on the provided reference corpus. It integrates technical perspectives on predictive modeling, optimization, and control with economic and institutional insights related to market behavior, risk assessment, and policy frameworks. The study elaborates in detail on methodological approaches used in recent literature, including supervised learning models, hybrid optimization techniques, reinforcement learning-based control systems, and BIM-integrated lifecycle assessments. It further explores how these approaches intersect with issues such as investment risk, willingness to pay, market signaling, and regulatory standards. By critically examining results reported across diverse empirical and theoretical studies, the article identifies key achievements, persistent limitations, and underexplored research gaps. The discussion emphasizes the need for interdisciplinary integration, transparent performance evaluation, and alignment between technological innovation and economic incentives. The article concludes by outlining future research directions that can enhance the robustness, scalability, and policy relevance of intelligent energy efficiency solutions in the building sector, particularly in the context of sustainable development and low-carbon transitions.