The integration of Internet of Things (IoT) with image processing presents transformative opportunities for sustainable agriculture amid growing environmental challenges and food security concerns. This research investigated how these combined technologies can optimize resource utilization while enhancing crop yield and reducing environmental impact. The study implemented a multi-layered technological framework comprising wireless sensor networks, multispectral imaging systems, and machine learning algorithms to create a comprehensive crop monitoring and management system. In methodology involved deploying networks of soil moisture sensors, temperature sensors, and nutrient analyzers across three agricultural zones (2.5 hectares each). Visual data was captured using both fixed-position cameras and drone-mounted multispectral cameras, with images processed through a custom-developed convolutional neural network achieving 87% classification accuracy for identifying common crop diseases. Results demonstrated significant improvements in agricultural efficiency: water usage decreased by 27.3% compared to conventional irrigation practices, while targeted pesticide application reduced chemical use by 41.6%. The system successfully identified early-stage crop diseases with 92.4% accuracy and provided automated irrigation recommendations maintaining optimal soil moisture levels within ±3% of targets. Crop yield increased by 18.9% in test zones compared to control areas using traditional farming methods. Economic analysis revealed that despite initial implementation costs averaging $4,800 per hectare, the system achieved return on investment within 2.4 growing seasons through resource savings and yield improvements. Challenges identified included data security vulnerabilities, interoperability issues between sensors, and the need for simplified user interfaces for broader adoption. This research demonstrates that IoT-integrated image processing systems can substantially advance agricultural sustainability through precision resource management while improving productivity and environmental outcomes. The findings support broader implementation of these technologies, particularly in regions facing water scarcity and increasing climate variability. Future research should focus on developing standardized protocols for data sharing and system integration to facilitate wider adoption across diverse agricultural settings, ultimately contributing to more resilient and sustainable food production systems aligned with global environmental goals.