The growing adoption of cloud-based Internet of Things (IoT) systems has introduced significant challenges in managing network latency, adversely impacting real-time applications like healthcare, smart cities, and industrial automation. As IoT devices are often geographically dispersed, transmitting data over long distances to centralized cloud servers leads to delays, reducing system responsiveness and reliability. To address this issue, this study explores several latency optimization methods, including the implementation of edge and fog computing, advanced routing protocols, and data compression techniques. Edge and fog computing architectures bring data processing closer to the IoT devices, reducing the distance data must travel and minimizing latency. In addition, advanced routing protocols and packet scheduling are utilized to optimize data transmission. In contrast, data compression techniques are applied to reduce transmitted data volume, further improving transmission speeds. The results demonstrate a substantial decrease in network latency, with edge and fog computing reducing latency by up to 40% compared to traditional cloud-only systems. These improvements are further enhanced by applying optimized routing and compression methods, which streamline data flow and increase transmission efficiency. The implications for the field are significant, as these solutions improve the performance and scalability of existing cloud-based IoT systems and enable the development of future latency-sensitive applications such as autonomous vehicles and real-time analytics, making cloud-based IoT deployments more viable for critical real-time tasks.