Our research intends to solve crucial issues such as agricultural production, regional inequities, inefficiencies in food distribution, and resource utilization. Despite challenges such as a lack of comprehensive data and limited engagement with government agencies and local communities, we have achieved tremendous progress in our methods. To date, we have successfully collected production statistics from each area as supplied by the High Commission for Planning. However, distribution and consumption statistics are currently lacking. To ensure accuracy and consistency across varied datasets, we conducted extensive analysis, standardization, and cleaning throughout the data preparation step. In the Data Analysis and Modelling stage, we’ve started looking into machine learning methods like ARIMA and recurrent neural networks for forecasting agricultural output trends. While our geospatial analysis has yet to be completely deployed, we want to use GIS approaches to optimize transportation routes and logistics. Moving ahead, our primary focus will be on creating optimization algorithms to improve resource allocation and eliminate surpluses and shortages. We want to build a prototype AI- powered food distribution system and use historical data to assess its effectiveness in optimizing food distribution. Despite the obstacles, our process is iterative and adaptive. We are committed to developing our methodology based on stakeholder feedback and real-world experiences, with the goal of increasing food security and access to market information in the regions we serve.