The rapid growth of digital collaboration platforms has led to an unprecedented increase in recorded meetings and conversational data. These interactions are commonly preserved as textual transcripts through automated transcription technologies. While transcripts provide a complete record of discussions, their unstructured and verbose nature limits their direct usefulness for organizational follow-up and decision-making. Among the most valuable outcomes of meetings are action items, which capture tasks, responsibilities, and commitments that must be executed after the discussion ends. This review paper examines the role of multi-agent artificial intelligence systems in extracting action items from meeting transcripts. By synthesizing recent advances in multi-agent frameworks, large language models, conversational analysis, and meeting intelligence, the paper highlights how agent-based decomposition improves accuracy, interpretability, and scalability in action item extraction. The review also discusses architectural patterns, coordination strategies, and application contexts, offering a structured understanding of how multi-agent approaches address the limitations of traditional single-model pipelines.