Agriculture is essential for economic stability, yet farmers often face challenges in crop selection and sourcing inputs. To address this, we propose a platform that combines a commodity exchange system with predictive analytics using the K-nearest neighbor (KNN) model to guide farmers on optimal crops for specific locations. The model assesses nine parameters: nitrogen, potassium, phosphorus, air quality, soil moisture, temperature, humidity, rainfall, and pH. By inputting these, farmers can forecast suitable crops, enhancing resource efficiency and yield. Additionally, the platform serves as a marketplace where farmers request essential inputs—fertilizers, seeds, equipment—while buyers such as wholesalers respond with their needs. This dual-purpose platform aligns supply with demand, minimizes waste, and fosters collaboration. By merging predictive analytics with a commodity exchange, we aim to improve crop production, resource allocation, and sustainable agricultural practices. Integrating real-time data with AI-driven recommendations empowers farmers with actionable insights that reduce risk and optimize decisions. It will also help to increase transparency in markets and access to allow smallholder farmers to compete more effectively in a rapidly changing agricultural landscape.