Heavy metal contamination remains a persistent environmental challenge due to the toxicity, non-biodegradability, and bioaccumulative nature of metals such as lead, cadmium, mercury, arsenic, and chromium. While conventional analytical techniques offer high sensitivity, their dependence on centralized laboratory infrastructure limits real-time and large-scale monitoring. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have enabled transformative improvements in sensor-based detection systems. This Perspective discusses how AI-driven electrochemical, optical, spectroscopic, and biosensor platforms enhance signal processing, multi-metal discrimination, concentration prediction, and anomaly detection under complex environmental conditions. The study highlights the role of supervised and deep learning architectures—including random forest, support vector machines, convolutional neural networks, and long short-term memory networks—in addressing nonlinear sensor responses and environmental variability. Beyond performance enhancement, the study also examines the challenges related to data standardization, model interpretability, deployment scalability, and regulatory acceptance. Emphasis is placed on the integration of AI-enabled sensors with Internet of Things (IoT) frameworks and remote sensing systems to enable real-time environmental surveillance and smart monitoring infrastructures. By bridging laboratory precision with field-deployable intelligence, AI-driven monitoring systems have the potential to support sustainable environmental governance, early warning mechanisms, and evidence-based policy decisions. Future progress will depend on explainable AI models, cross-regional transfer learning, and robust validation under real-world conditions.
Priyanka Wagh (Sun,) studied this question.