Large Language Models (LLMs) are transforming how organizations manage and understand their processes. Recent conceptual studies have outlined opportunities for integrating LLMs throughout the BPM lifecycle. Prior structured reviews offer initial insights into practical application, but the rapidly evolving nature of the field requires continuous reassessment to maintain clarity on existing solutions, encountered challenges, and opportunities within each BPM lifecycle phase. Through a structured review of recent studies, we analyze how LLMs are applied across all BPM lifecycle phases. We specifically address three aspects per phase: contribution, implementation, and evaluation. Based on this, research and practical opportunities are identified. Our analysis shows that LLMs can integrate heterogeneous process documentation, generate process models from natural language, support process automation through executable outputs, and enable more accessible analysis and monitoring. Common technical approaches and recurring challenges, notably prompt sensitivity and scalability constraints, are identified. By connecting theoretical opportunities with practical applications, we offer a synthesized view of LLMs role in BPM and outline implications for future research and system development.