The global crop productivity is declining due to several factors. Among those issues, weed invasion is the main biotic limitation, and it is efficiently manageable through cultural, physical, biological and chemical means. Among all the weed control methods, hand weeding is the most efficient one. Additionally, experts are under pressure to discover a solution due to rising labor costs and a shortage of workers during peak periods of farm operations. The lack of available labors has led to a significant decline in crop production in recent years. In the aforementioned situation, controlling weeds with chemicals is the most practical and cost-effective approach. But in recent times, widespread issues with food and environmental contamination have been progressively brought on by the overuse and improper application of chemical pesticides. Research in agriculture is shifting to focus on strategies that can mitigate the adverse consequences of the shortage of labor through the hastened completion of agricultural operations and the precise application of agrochemicals. Because machine learning greatly simplifies the categorization procedure, there are several commercially accessible artificial intelligence (AI)-based tools and technologies to manage weeds, including robotics, spectral analysis, and remote sensing. Despite the exceptional effectiveness of AI-based strategies against weeds, the constraints associated with these technologies still restrict their real-world applications. In this chapter, we have discussed the significance of using AI for weed management strategies and the applications of AI technology for the mapping, identification, and control of weeds. We anticipate the concepts will serve as a platform for the development and implementation of tried-and-true AI-based technologies in order to manage more weed species worldwide.