The transition to sustainable construction practices is necessary to address the escalating environmental challenges of the built environment. In this context, the integration of advanced digital technologies, particularly Building Information Modeling (BIM) and Artificial Intelligence (AI)—offers significant potential to enhance energy efficiency and reduce the carbon footprint of buildings. However, the convergence of BIM and AI in sustainability-driven workflows remains underexplored. Key research questions include: (1) What are the emerging trends and future directions for integrating BIM and AI into energy simulations and Life Cycle Analysis (LCA) ? And (2) What technical and methodological barriers hinder their effective implementation? To address these questions, a systematic literature review was conducted, synthesizing current approaches, tools, models, and case studies. The findings highlight substantial progress in data integration, predictive analytics, and automated energy modeling. Nonetheless, challenges persist—particularly in managing multi-criteria optimization involving the simultaneous evaluation of energy, cost, environmental, and regulatory objectives. The study reveals a growing body of research focused on integrated BIM-AI systems capable of supporting nuanced trade-offs between sustainability goals. In response, a conceptual framework is proposed to guide researchers and practitioners in implementing these technologies effectively. This work provides an up-to-date synthesis of the state-of-the-art in BIM-AI integration for energy simulation and LCA, while emphasizing the strategic role of multi-objective optimization. Future research should focus on enhancing algorithmic robustness, improving digital tool interoperability, and aligning with evolving policy and regulatory frameworks.