The integration of Internet of Things machinery and deep learning has changed smart agriculture, showing inventive solutions for weed anxiety and management. Customary weed control processes are labor intensive and often environmentally unsustainable. This paper explores streamlined deep learning styles tailored to weed detection in IoT-driven smart farming systems. Using complex convolutional neural networks and transfer learning, these techniques are used to accurately identify plants in real time. The integration of IoT systems enables efficient data collection and processing from agricultural settings, resulting in prompt actions with minimal human intervention. These energy-constriction Ir reducible model architectures are designed to achieve a balance between Computer-aided computational efficiency and high observation detection accuracy. The implementation of advanced data augmentation and edge computing techniques boosts the strength in models, as well as decreasing dependence on concentrated centralized cloud systems. This study showcases the role that these combined technologies can play in reducing herbicide usage, enhancing crop productivity, and encouraging sustainable farming practices. By addressing critical challenges like data scarcity, model scalability, and real-time decision making, this study provides a comprehensive framework for deploying cost-effective weed management solutions in precision agriculture, ultimately contributing to more sustainable and eco-friendly farming methods.