The present invention provides a system and method for forecasting aircraft engine maintenance using a Multi-Agent Reinforcement Learning (MARL) framework. The system extracts operational sensor data from aircraft engines, processes the data through multiple collaborative RL agents, and outputs optimal maintenance decisions based on predicted RUL values. The invention further introduces a novel reward structure utilizing Adaptive Risk-Based Reward and Stepwise Reward mechanisms to improve predic