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AI‐powered monitoring platforms can significantly enhance the accessibility and responsiveness of water quality assessment in decentralized and resource‐limited settings. Conventional methods for detecting heavy metal ions, such as atomic absorption spectroscopy (AAS), offer high accuracy but require expensive instrumentation, trained personnel, and laboratory infrastructure, limiting their use in field applications. Here, SmartDetectAI, a low‐cost, portable, AI‐powered web application designed for rapid, on‐site colorimetric detection of heavy metal ions in water is presented. The system integrates silver nanoparticles (AgNPs) prepared from plant extract with a custom‐built imaging chamber and a web‐based application (web app) for automated and remote analysis. Supported by a computer vision model (YOLOv8n) for region detection and a machine learning algorithm (XGBoost) for concentration estimation, SmartDetectAI enables automated, real‐time quantification of mercury‐ and cadmium‐based species, which are the predominant aqueous forms under near‐neutral pH conditions. Users capture sensor images with a smart device and receive result outputs through an intuitive graphical interface hosted on a Flask‐based server. Field validation using pond water samples spiked with 1 and 10 μM Cd2+ shows strong agreement with standard AAS measurements, achieving an average predictive accuracy of ≈84%. SmartDetectAI integrates silver nanoparticle‐based colorimetric sensing with an AI‐powered web app for rapid, on‐site detection of toxic heavy metals in water. By combining aggregation‐driven optical changes with machine learning analysis of red ‐ green ‐ blue values, the platform achieves portable, low‐cost, and accurate monitoring of Hg‐ and Cd‐based species, validated against atomic absorption spectroscopy in real water samples.