Speaker recognition and diarization are crucial technologies that enable machines to identify who is speaking and when adding a layer of contextual understanding to speech processing systems. This chapter explores how reinforcement learning (RL) is transforming these fields, addressing challenges such as adapting to new speakers, handling overlapping speech, and operating in real time with limited computational resources. We’ll delve into innovative RL-based approaches that are making speaker recognition and diarization systems more accurate, adaptable, and efficient. Through case studies and real-world applications, we’ll demonstrate how these advancements are enhancing everything from security systems to personalized voice assistants. As we navigate this exciting landscape, we’ll also examine how improvements in speaker recognition and diarization synergize with other areas of speech and language technology, setting the stage for more sophisticated and context-aware AI systems.