Globally, weeds are the major obstacle to sustainable crop production, resulting in lower yields, higher labour and chemical input costs, and environmental damage from overuse of herbicides and the ensuing resistance. Rapid developments in robots, artificial intelligence (AI), sensor technology, precision agriculture, and creative non-chemical techniques over the past ten years have opened up new avenues for integrated weed management (IWM). Deep learning-based weed/crop classification, unmanned aerial vehicle (UAV) and hyperspectral imaging, ground robotic weeders, Harvest Weed Seed Control (HWSC), and innovative thermal, laser, steam, and electrical spot treatments are just a few of the recent technological advancements in weed detection, removal, and resistance mitigation that are highlighted in this review. Particularly for large-scale and smallholder systems, we evaluate their efficacy, constraints, cost-benefit factors, and adoption paths. Additionally, we present a flowchart and decision framework for selecting suitable weed management technologies based on crop type, weed species, farm size, and economic, regulatory, and environmental constraints. Finally, we identify research gaps and future directions necessary to ensure these technologies contribute to sustainable weed control, reduced herbicide reliance, and biodiversity conservation.