The rapid evolution in Artificial Intelligence and advancements in technology is creating a major divide between academic programs and industry expectations. The traditional methods involve extensive human intervention tasks such as manual curriculum mapping using spreadsheets, that are interpreted by expert review panel. The board committee identifies skill gap with multiple surveys and feedback, often time consuming and this complicates the alignment process of skill set matching with program development. To overcome this problem we proposed Adaptive Curriculum Maps. This study aims to foster alignment with an adaptive framework that blends graph-based knowledge representations with the reasoning power of Large Language Models (LLMs) to mitigate these gaps. Graph Augmented Retriever Oriented LLM (GARO-LLM) is a hybrid orchestrator that keeps academic programs adaptive by unifying text Retrieval Augmented Generation (RAG) from various unstructured sources. These sources represent information such as job postings, scholarly articles, professional networks and forums, tech news, and media outlets. The system begins by constructing a concept graph that maps relationships between fundamental topics. GARO-LLM addresses a practical shortcoming for departments and curriculum committees as it produces catalog-ready proposals by addressing gaps in core competencies. Smart Curriculum Maps are dynamic and data-driven, as compared to traditional methods, and adjust pathways as student’s progress or when there is a shift in market dynamics. This helps in faster market alignment, better opportunities, being ready for professional challenges, and preserving pedagogical balance. Preliminary exploration indicates that GARO-LLMs could become a practical foundation for the next generation of curriculum design.