Abstract: This study introduces a comprehensive AI framework aimed at enhancing agricultural supply chain management via the combined utilization of four machine learning models. The system utilizes a Long Short-Term Memory (LSTM) network for accurate demand forecasting, a Reinforcement Learning (RL) model for dynamic logistics optimization, a Convolutional Neural Network (CNN) for automated quality control through computer vision, and an XGBoost-based ensemble for real-time decision support.