Global food security depends critically on the agricultural supply chain, which is also rather sensitive to disturbances brought about by demand changes, extreme weather, and delayed transit. This work suggests a new hybrid architecture to improve agricultural supply chain resilience by combining Deep Reinforcement Learning (DRL) with Genetic Algorithms (GA). DRL is used to create adaptive rules for real-time decision-making; GA is used to maximize the structural configuration of the supply chain, including warehouse locations and transportation paths. Computational simulations running under several disturbance scenarios—including demand spikes and transit delays—verified the approach. A 15% operating cost savings and a 20% increase in adaptability measures indicate that the GA-DRL framework beats conventional approaches. Under dynamic conditions, the hybrid architecture also shows faster recovery times and better service levels. These results show the complementing strengths of GA and DRL, in which DRL offers the flexibility required for real-time adaptation while GA guarantees a strong starting configuration. This work not only solves important problems with agricultural supply chain resilience but also provides a scalable method for handling intricate and uncertain systems. Investigating multi-objective optimization methods and merging real-time data streams can help to improve system responsiveness and sustainability even further in future directions.