Computation intelligence-based workload prediction in edge computing is a rapidly evolving research area that focuses on leveraging intelligent techniques to forecast task demands and optimize resource allocation in dynamic and resource-constrained edge environments. Research papers in this domain explore machine learning, deep learning, fuzzy logic, neural networks, and hybrid computational intelligence approaches to predict workload patterns generated by applications such as IoT services, autonomous vehicles, smart healthcare, and industrial automation. Studies emphasize the importance of accurate workload prediction for proactive resource provisioning, task scheduling, load balancing, energy efficiency, and latency reduction. Recent works also integrate prediction models with edge–fog–cloud collaboration, adaptive offloading strategies, and service orchestration frameworks to enhance Quality of Service (QoS) and Quality of Experience (QoE). Security- and privacy-aware prediction frameworks are increasingly investigated to protect sensitive data while maintaining high prediction accuracy. Overall, computation intelligence-based workload prediction in edge computing enables intelligent, adaptive, and efficient management of distributed resources, ensuring optimized performance and scalability in next-generation edge computing ecosystems.