Speaker diarization is an AI-driven process that segments continuous audio streams by individual speakers, providing essential insights for various industries, including call centers, meeting platforms, and media companies. This technology enhances conversation analysis by generating actionable insights and improving accessibility and engagement through accurate speaker labeling. Evaluating speaker diarization quality involves metrics like Diarization Error Rate (DER), speaker count accuracy, overlapping speech handling, and temporal precision. Recent advancements have improved performance in challenging audio conditions, with systems like AssemblyAI and Gladia offering commercial APIs with robust capabilities, while open-source solutions like Pyannote and NVIDIA NeMo provide flexibility for research and customization. The choice of a diarization solution should align with specific technical requirements, deployment scenarios, and business constraints, considering factors such as accuracy, latency, security certifications, and total cost of ownership. As technology evolves, modern systems increasingly address real-world audio challenges, enabling more effective audio AI applications.