The field of task offloading in dynamic networks is moving towards developing innovative solutions that address the challenges of resource constraints, latency, and energy efficiency. Recent developments have focused on integrating artificial intelligence and machine learning techniques to optimize task offloading decisions and resource allocation. A key direction in this field is the use of hybrid approaches that combine different techniques such as supervised learning, reinforcement learning, and optimization algorithms to achieve efficient task offloading. Noteworthy papers include: Joint Resource Estimation and Trajectory Optimization for eVTOL-involved CR network, which proposes a trajectory optimization framework for eVTOL swarms that maximizes task offloading success probability while minimizing energy consumption and resource competition. Intelligent Task Offloading in VANETs, which presents a hybrid AI framework that integrates supervised learning, reinforcement learning, and Particle Swarm Optimization for intelligent task offloading and resource allocation. TinyMA-IEI-PPO, which proposes a novel framework for efficient Vehicular Embodied Agent AI Twins migration in VEANETs, combining a multi-leader multi-follower Stackelberg game-theoretic incentive mechanism with a tiny multi-agent deep reinforcement learning algorithm.