Speaker diarization is a crucial process that enables AI and humans to understand who is saying what throughout conversations, making it easier to extract valuable insights from audio recordings. This technique involves breaking up captured conversations into segments belonging to individual speakers, using algorithms that analyze features such as pitch, zero-crossing rate, and speaker clustering. Speaker diarization has various applications in industries like customer service, sales, and support calls, allowing AI systems to provide better experiences by suggesting questions to ask, making real-time inferences, and improving performance through offline analysis. The technique can be performed using either deterministic or probabilistic approaches, with deep learning-based methods proving to be highly effective, especially when combined with supervised training that leverages labeled data and random forest algorithms.