With the rapid growth of multimedia content and online gaming, traditional cloud computing faces difficulties in meeting real-time requirements, particularly in optimizing caching strategies, managing dynamic environments, and handling user location uncertainties. To overcome these limitations, we propose a novel AI-Enhanced Edge Cooperation framework based on Multi-Agent Deep Reinforcement Learning (EC-MADRL) to optimize scheduling and resource allocation across edge nodes. This framework enables adaptive caching and replenishment strategies in a cooperative environment, modeled as a multi-agent Markov Decision Process (MDP). By integrating an online MADRL approach, the EC-MADRL algorithm allows edge nodes to collaboratively learn optimal policies for caching and resource distribution. We analyze the time complexity and convergence of the algorithm, demonstrating its effectiveness in improving edge node performance. Experimental results show a 30.12% increase in edge node profits, a 20.67% reduction in user latency, and a 24.31% decrease in user costs, highlighting the superior performance of our AI-enhanced approach over baseline methods, including DDQN-ECMP, MADRL-ECMP, and P4LRU algorithms.