Weed infestation is a major challenge in agriculture, significantly affecting crop yield, quality, and overall farm productivity. Conventional weed detection methods, such as manual inspection and uniform herbicide application, are labor-intensive, time-consuming, and environmentally harmful. With recent advancements in Artificial Intelligence (AI) and computer vision, automated and precise weed detection has become feasible. This paper presents an AI-based approach for detecting weed plants in agricultural fields using deep learning techniques. Field images captured through cameras or unmanned aerial vehicles are processed using convolutional neural networks to accurately differentiate weeds from crops. The proposed system focuses on efficient feature extraction, robust classification, and reliable weed localization under varying field conditions. The AI-based approach supports precision agriculture by enabling targeted weed control, reducing chemical usage, minimizing labor costs, and promoting sustainable farming practices. The results demonstrate the potential of AI techniques to improve weed management efficiency and enhance agricultural productivity.