Edge and Fog computing optimize IoT data processing near the source, crucial for applications needing low latency like autonomous vehicles. Key challenges include resource constraints and data heterogeneity. Optimization relies on AI, Deep Reinforcement Learning for adaptive task scheduling, and Federated Learning with Differential Privacy and Blockchain for secure, privacy-preserving model training and resource management across distributed networks.