The convergence of cloud computing and telecommunications networks is transforming the architecture of modern networks, enabling the deployment of novel services that require ultra-reliable low-latency communication (URLLC). Network slicing, which allows the creation of multiple virtual networks with differing capabilities on a single physical infrastructure, is key to meeting the diverse requirements of URLLC services. Artificial Intelligence (AI) has emerged as a crucial technology in optimizing network slicing, allowing dynamic resource allocation, real-time monitoring, and intelligent decision-making to meet stringent latency and reliability requirements. This article provides a comprehensive review of AI-powered network slicing in cloud-telecom convergence, with a focus on URLLC. It explores the state-of-the-art in AI applications for network slicing, presents a case study to demonstrate its effectiveness, and discusses the challenges and future directions in this domain.