The interconnected risks to interdependent infrastructure, environmental, and socioeconomic systems posed by climate change, energy transitions, and sustainable development require transdisciplinary perspectives to understand the involved complex dynamics and interdependencies. The breadth and diversity of systems, processes, and risks require a synthesis of an extremely diverse set of research fields, literatures, and operational expertise. Recent breakthroughs in artificial intelligence (AI) present promising opportunities to accelerate progress in transdisciplinary synthesis in MultiSector Dynamics (MSD) research. AI can potentially help to clarify connections across scientific communities and accelerate the translation of insights across domains. Here we demonstrate a systematic approach using a combination of modern natural language processing (NLP), graph-based and other machine learning approaches to gain on-demand topical access to, and insight from, a corpus of over 100,000 scientific publications and other ancillary data sources that are representative of the relevant literature landscape for the field of MSD. These insights help us identify stable and emerging communities of researchers and research topics that align with advancing the aspirations of the MSD community. We identify and describe advances in the cross-domain bodies of literature addressing the interconnected sustainability and climate change risks across scales, sectors, and systems. Our analysis seeks to quickly understand gaps and opportunities that currently exist for MSD researchers. We provide these state-of-the-art AI/ML/NLP workflows to the MSD community as we believe that cross-disciplinary training and teaming is critical for advancing complex adaptive human-Earth systems science in a world of deeply uncertain and interconnected risks.