Weeds, unwanted plants that grow spontaneously in agriculture fields, pose significant threats to crop productivity by extracting essential water and nutrients from the soil, impeding cultivated crop growth. Weed growth is rapid (mainly through seeds, rhizomes, or other vegetative structures), which makes their spread easier. Despite adopting advanced agricultural practices such as improved seeds, fertilizers, and irrigation, farmers often overlook weed control, reducing crop yields and potential financial losses. This necessitates the requirements of weed management. While manual weed management is labor-intensive and expensive, chemical methods are harmful to both health and the environment. To address these challenges, technology-driven weed management frameworks are increasingly necessary. Nowadays, herbicides are used to control weeds through UAVs. Spot spraying, variable-rate spraying, or a portion of the field that does not allow heavy equipment to get in the field can all be accomplished easily with drone spraying technology. Still, several challenges remain open, including optimizing the herbicide spray and mitigating wind drift effects. To address these challenges, our study introduces an Internet of Robotic Things (IoRT)-enabled UAVs system leveraging Genetic Algorithms (GA) for optimized flight paths and Particle Swarm Optimization (PSO) for dynamic nozzle control. This system ensures precise herbicide application, even under challenging conditions, by dynamically adjusting UAV operations in real-time based on environmental factors such as wind speed and direction. Our experimental results demonstrate the effectiveness of this approach in enhancing weed management practices.