Deep learning (DL) is transforming weed management by enabling precise, automated detection and classification, crucial for site-specific weed management (SSWM). This approach minimizes herbicide use and environmental impact, addressing the substantial global economic losses (USD 75.6 billion annually) caused by weeds. DL models, often integrated with UAVs and ground robots, utilize architectures like YOLO and Mask R-CNN for accurate weed identification and targeted intervention. While beneficial, challenges include data scarcity, the "green-on-green" problem, and real-time performance on edge devices. Future directions involve adaptive frameworks, cross-domain transfer learning, and hybrid DL architectures to enhance robustness and generalization.