Meta-heuristic based scheduling and load balancing in fog computing is a rapidly evolving research area that leverages advanced optimization techniques to address the complexity of distributing tasks and balancing workloads across heterogeneous fog nodes. Research papers in this domain explore the application of genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), simulated annealing (SA), grey wolf optimization (GWO), firefly algorithms, and hybrid meta-heuristics to efficiently minimize execution delay, energy consumption, and service cost while improving Quality of Service (QoS). Studies highlight multi-objective scheduling and load balancing frameworks that jointly optimize task placement, resource allocation, and migration strategies under dynamic and uncertain fog environments. Recent works also integrate machine learning with meta-heuristics to design adaptive, context-aware, and predictive scheduling algorithms. Security- and reliability-aware approaches are increasingly emphasized to ensure that load balancing not only enhances performance but also strengthens system robustness against failures and cyber threats. Applications of these techniques are seen in latency-critical IoT domains such as smart healthcare, intelligent transportation, industrial IoT, and real-time multimedia processing. Overall, research in meta-heuristic based scheduling and load balancing in fog computing provides scalable, intelligent, and energy-efficient solutions for managing the inherent complexity of distributed fog infrastructures.