This project introduces a centralized system for monitoring and optimizing the management of horticultural resources in the agricultural sector. It addresses the imbalance between supply and demand, which often leads to financial losses for farmers and higher costs for consumers. The system supports local governments in tracking resources through real-time data visualization and predictive analytics. It employs Linear Regression for forecasting and K-Means clustering for resource categorization, enabling better planning and distribution. By integrating data-driven techniques, the platform improves decision-making, reduces waste, and enhances overall market efficiency. The system demonstrates how predictive modeling and clustering can significantly improve agricultural resource management.