Soil and water contamination by heavy metals and other pollutants is a rising global crisis threatening agricultural productivity, ecosystem health, and human wellbeing. Phytoremediation, the use of plants to extract, stabilise, or transform environmental contaminants, offers an economically viable and ecologically sustainable alternative to conventional remediation technologies. However, traditional phytoremediation faces substantial constraints, including species selection uncertainties, extended remediation timelines, monitoring challenges, and limited predictive capacity. Advances in artificial intelligence (AI) technologies create unprecedented opportunities to overcome these operational bottlenecks whilst enhancing remediation efficiency and sustainability outcomes. This chapter examined the integration of AI with phytoremediation at the intersection of hydro(geo)logy, environmental science, computer science, and sustainable development. The analysis first established phytoremediation fundamentals before systematically evaluating how specific AI technologies address several critical constraints. Machine learning algorithms enable intelligent species selection via integration of genomic, phenotypic, and environmental data; remote sensing and hyperspectral imaging facilitate non-invasive contamination assessment and spatial mapping; Internet of Things (IoT) sensor networks coupled with AI analytics enable real-time monitoring and adaptive management; whilst reinforcement learning optimises growth conditions, harvest timing, and resource allocation. Emerging technologies including blockchain verification and digital twin simulation promise further enhancements. The chapter explains how AI-supported phytoremediation contributes to achieving multiple Sustainable Development Goals, including clean water and sanitation (SDG 6), sustainable cities (SDG 11), responsible production (SDG 12), climate action (SDG 13), and ecosystem restoration (SDG 14, 15). Implementation pathways, technical and socioeconomic barriers, ethical considerations, and research frontiers are analysed, emphasising on public-private partnerships, multi-stakeholder collaboration, and phased scaling strategies. A roadmap for mainstreaming AI-enhanced phytoremediation by 2030 identified immediate priorities. The synthesis indicated that whilst transformative potential exists, realising widespread adoption needs unified action. As AI-supported phytoremediation offers credible, scalable pathways for advancing environmental sustainability and public health protection, it becomes critical to thoughtfully integrate computational intelligence with the nature-based solutions.