Background: The article provides an overview of existing techniques, their advantages and disadvantages, and their applicability in fog computing. Anomaly detection techniques in fog computing devices are crucial for ensuring the reliable and safe operation of fog computing environments. Problem: The scattered and heterogeneous nature of fog computing, on the other hand, creates a number of concerns, including possible security risks and operational uncertainty. Furthermore, the paper outlines numerous outstanding issues and research objectives, such as enhancing the approaches’ accuracy, scalability, and resilience, creating techniques that can effectively identify multiple types of anomalies, and developing strategies that can be used in real-world scenarios. Scope: Anomaly detection techniques play a pivotal role in mitigating these challenges by identifying unusual behaviors that may indicate security breaches, faults, or deviations from normal operational patterns. Furthermore, the review identifies several open challenges and research directions, such as improving the accuracy, scalability, and robustness of the techniques, developing techniques that can efficiently detect multiple types of anomalies, and developing techniques that can be deployed in large-scale fog computing networks. This review gives useful insights into existing anomaly detection approaches. To handle IoT networks, Fog computing systems require unique SLAs, bandwidth-aware design, and scalability. Fog-resource monitoring, green computing, and Federated Reinforcement Learning (FRL) can improve energy consumption and reliability. Aim: The aim of anomaly detection for fog computing devices is to enhance the overall performance, privacy, security, and stability of fog computing environments by promptly identifying and addressing anomalies.