With the growing volume of audio and video content generated during both virtual and in-person meetings, there is an increasing need for efficient analysis tools to support decision-making. While recent research has addressed individual tasks such as transcription, speaker identification, and summarization, these components are often developed and applied independently. This work introduces a unified, machine learning-assisted, semi-automated pipeline that integrates these tasks into a cohesive system. The proposed method enables real-time transcription, speaker diarization, and summarization, offering a deeper understanding of meeting dynamics. Unlike traditional tools that lack adaptability and operate in isolation, our approach incorporates a Human-in-the-Loop (HITL) interface for user validation and refinement, enhancing both accuracy and flexibility. By leveraging state-of-the-art speech recognition, speaker embedding models, and topic modeling techniques, our system provides actionable insights from raw meeting recordings with minimal manual intervention. This integrated solution marks a significant advancement in meeting analysis by effectively combining automation with human oversight.