Abstract: In 21st century, the rapid increase in population and industrialization not only limits the per capita arable land for crop production but also limits the productive potential of soil and agricultural crops due to the negative impacts of anthropogenic climate change. Besides the abiotic factors of the environment, among biotic factors limiting productivity, weeds contribute the maximum. Due to various limitations in conventional weed control methods, integrated weed management (IWM) practices have evolved for effective weed management in agriculture. In this era of information and technological evolution, artificial intelligence is moving at a faster pace in every sector to address the issues of various dimensions. The use of deep learning, machine learning, and artificial neural networks in AI-enabled robots and unmanned aerial vehicles, along with multi- and hyper-spectral image sensors, make the tools capable enough for quick and efficient weed management for harnessing the ultimate productive potential of different fields crops. No doubt, the IWM practices designed for various crops in different countries in different ecologies have advantages over the individual and traditional approaches to weed control, but the use of these AI-enabled software and tools can save time, resources, money, and labor when used along with the best IWM method. Sensor-based weed identification, mapping, and automation can be done for precise and effective management of weed flora using these modern approaches, which will be environmentally friendly and have a broader scope for achieving global food security.