Weed management is an important component of contemporary agriculture, having a direct influence on crop yield, soil quality, and farm productivity as a whole. Conventional methods of weed control, including manual weeding and chemical herbicides, are labor-intensive, environmentally demanding, and tend to cause herbicide resistance among weed populations. Automated weed control systems that combine artificial intelligence (AI), robotics, machine vision, and precision application technologies are transforming weed management by providing effective, environmentally friendly, and cost-effective solutions. These systems employ sensors, drones, autonomous robots, and intelligent sprayers to sense, identify, and selectively eliminate weeds with high precision. Deep learning and computer vision advancements allow for real-time discrimination of weeds from crops, minimizing herbicide use and environmental disturbance. The current developments in automated weed control technologies, their benefits, limitations, and potential for the future in sustainable agriculture are the subject of this review. The implementation of these innovative systems increases the efficiency of farm operations, reduces dependence on chemical inputs, and contributes to the goal of precision agriculture to promote food security and environmental sustainability.