Fog and cloud computing has revolutionized distributed systems through solving significant challenges like resource management, latency, and scale. The main characteristic of fog computing is to bring the computational resources close to the data source to allow near real-time processing for delay sensitive applications, improving the response time for those applications, while cloud computing centralizes the data for long life storage and massive processing. In this manner, these paradigms interoperate to yield hybrid architectures that address the increasing demands of networked systems such as the Internet of Things. In this review we listed the development, models, issues and parallel processing in fog and cloud computing. Energy-efficient task scheduling, privacy preserving models, and fault-tolerant designs are some advancements that improve system reliability and performance. Additionally, containerized microservices and federated learning approaches also enable seamless integration and secure data management in various applications. However, there are still issues with strong interoperability, preserving the performance of a system under extreme load, and reducing security threats even with great advances. We analyze the gaps, propose solutions, and emphasize the key role of adaptive frameworks and innovative resource allocation methods in tackling those gaps. These results show how fog and cloud computing can change the landscape of distributed systems in the future.
