Chromium (Cr) toxicity, primarily from hexavalent Cr (Cr(VI)), poses a significant and widespread environmental issue resulting from heavy industrial activities such as tanning, electroplating, and mining. Traditional monitoring methods are often labor-intensive, slow, and limited in coverage, which hinders effective and real-time management of contamination. The adoption of Artificial Intelligence (AI) and the Internet of Things (IoT) marks a significant change, shifting from passive observation to proactive, intelligent environmental oversight. This chapter thoroughly examines how AI and IoT-based technologies are transforming the tracking and control of ecological chromium toxicity. It details the environmental chemistry and toxicological effects of chromium, underscoring the urgent need for advanced solutions. The chapter also explores the design of IoT sensor networks, emphasizing sophisticated sensing platforms for in-situ, continuous, and multiplexed detection of chromium compounds in soil, water, and air. It primarily discusses how AI and machine learning (ML) algorithms can be applied, including supervised learning for predicting concentration, unsupervised learning for identifying contamination sources, and deep learning for analyzing complex spectral data from sensors. Additionally, it examines how these technologies can be integrated into innovative decision-support systems for predictive pollution modeling, automated remediation, and risk evaluation. The chapter also highlights key challenges, including sensor calibration, data security, energy efficiency, and model transparency. Finally, it considers prospects, including the potential of digital twins for ecosystems affected by chromium and how these digital tools can promote a circular economy. This work aims to offer researchers, environmental engineers, and policymakers a solid foundation and guidance for leveraging advanced digital technologies to address the global issue of chromium pollution.