Application and service placement in fog computing is a vital research area that focuses on determining the optimal deployment of applications and services across distributed fog nodes to meet latency, Quality of Service (QoS), energy efficiency, and resource utilization requirements. Research papers in this domain explore static, dynamic, and adaptive placement strategies that consider heterogeneous node capabilities, network conditions, fluctuating workloads, and application-specific constraints. Studies highlight heuristic algorithms, metaheuristic approaches, optimization models, and machine learning techniques—including reinforcement learning and predictive analytics—for intelligent, context-aware, and performance-efficient application and service placement. Recent works also investigate multi-tier fog–edge–cloud architectures to enhance scalability, fault tolerance, and service continuity while minimizing latency and operational overhead. Security- and privacy-aware placement frameworks are increasingly emphasized to protect sensitive data during deployment and execution. Applications span smart healthcare, autonomous vehicles, industrial IoT, smart cities, and latency-sensitive multimedia services, where optimal placement is critical for system performance and reliability. Overall, research in application and service placement in fog computing enables adaptive, efficient, and resilient deployment strategies, ensuring high performance, energy efficiency, and reliable service delivery in next-generation fog computing ecosystems.