The interconnected risks posed by climate change, energy transitions, and sustainable development require transdisciplinary perspectives to comprehend the complex dynamics and interdependencies involved. Recent advancements in artificial intelligence (AI) offer promising opportunities to accelerate MultiSector Dynamics (MSD) research. The integration of AI with established techniques from fields such as natural language processing (NLP) and graph theory can uncover hidden connections, extract causal relationships, and reveal insights across domains. This presentation will present a systematic approach that uses modern NLP, graph-based methods, and other machine learning techniques, including the integration of large language models, to provide on-demand topical access and insights from a corpus of over 100,000 scientific publications and other ancillary data sources. These resources help to map the literature landscape for the field of MSD and guide further exploration of opportunities for growth and maturation. Additionally, we will demonstrate the use of this systematic review to examine the evolution of machine learning and AI applications in MSD research through time and identify areas where these technologies can enhance current research activities.