Placement methods in edge computing are a significant research area that focuses on determining the optimal allocation and positioning of applications, services, and computational tasks across distributed edge, fog, and cloud resources. Research papers in this domain investigate strategies to minimize latency, reduce energy consumption, improve resource utilization, and ensure Quality of Service (QoS) for latency-sensitive and resource-intensive applications such as autonomous vehicles, smart healthcare, industrial IoT, and immersive media. Studies highlight static, dynamic, and hybrid placement methods, including heuristic algorithms, optimization techniques, game-theoretic approaches, and machine learning-based solutions to handle heterogeneous edge nodes, varying workloads, and user mobility. Recent works also explore service replication, migration, and multi-tier edge–fog–cloud collaboration to enhance scalability, reliability, and fault tolerance. Security- and privacy-aware placement strategies are increasingly incorporated to protect sensitive data while maintaining performance. Overall, research in placement methods for edge computing enables intelligent, adaptive, and efficient deployment of services and workloads, ensuring optimized performance, resource utilization, and resilience in distributed edge infrastructures.