Weeds in wheat (Triticum aestivum L.) farming are one of the important biotic stress factors that cause yield loss. Chemical herbicides, which are widely used in traditional weed management, cause problems such as herbicide resistance, environmental pollution, and high costs. Therefore, the need for sustainable and efficient agricultural practices is increasing. In recent years, smart technologies such as artificial intelligence (AI), machine learning (ML), unmanned aerial vehicles (UAVs), robotic systems, and remote sensing have offered innovative solutions for weed management in wheat. In this section, traditional and modern methods used in weed management in wheat farming are compared, and the effectiveness of artificial intelligence-supported systems, hyperspectral imaging, geographic information systems (GIS), and sensor-based technologies is evaluated. Artificial intelligence algorithms and deep learning models can detect problematic weeds in wheat with high accuracy and contribute to developing targeted control strategies. UAVs optimize herbicide use by performing weed mapping with high-resolution imaging and spectral analysis methods. Robotic systems reduce labor costs and support agricultural sustainability by directly controlling problem weeds in wheat through mechanical or selective chemical applications. As a result, integrating artificial intelligence and other advanced smart technologies offers environmentally friendly and economical solutions for wheat weed management. With the widespread use of these smart technologies, it is anticipated that it will be possible to reduce chemical inputs in wheat, increase wheat yield, and protect the ecosystem balance.