Food security challenges require innovative agricultural solutions that optimize resource utilization while maintaining environmental sustainability. This study develops and tests an IoT-based crop monitoring system integrating soil sensors, weather stations, and satellite imagery for precision agriculture. We deployed 120 sensor nodes across 45 farm sites in India, Mexico, and Ghana, monitoring wheat, corn, and cocoa crops respectively. Machine learning algorithms processed 2.8 million data points to optimize irrigation, fertilization, and pest management. Results demonstrate 32% water savings, 28% fertilizer reduction, and 19% yield improvements compared to conventional farming. The low-cost system ($89 per hectare) provides accessible technology for smallholder farmers.