<![CDATA[Rapid urban expansion and climate anomalies have intensified localized heat stress, demanding precise microclimatic tracking and proactive public health measures. Conventional observation networks often lack the spatial density and real-time predictive capabilities required for timely intervention. To address this challenge, this study presents the design, field implementation, metrological validation, and predictive evaluation of an Internet of Things (IoT) monitoring system integrated with eXtreme Gradient Boosting (XGBoost) for short-term Temperature-Humidity Index (THI) forecasting. The physical architecture employs a calibrated DHT22 sensor enclosed within a protective Stevenson screen and connected to an ESP32 processing node, transmitting continuous microclimatic metrics wirelessly to a Firebase cloud database. Sensor calibration against primary national standards confirmed high operational accuracy, yielding expanded uncertainties within -0.14°C to +0.18°C for ambient temperature and -1.21% to +1.76% for relative humidity, fully satisfying World Meteorological Organization (WMO) operational tolerances. Telemetry network stability evaluated under Telecommunications and Internet Protocol Harmonization Over Network (TIPHON) benchmarks demonstrated excellent performance, characterized by a low latency of 88.29 ms, packet jitter of 21.85 ms, and a high data throughput of 80293.43 bps. Furthermore, the cloud-integrated XGBoost regression model achieved exceptional accuracy in forecasting short-term thermal trends, yielding a Mean Absolute Error (MAE) of 0.1580, Root Mean Square Error (RMSE) of 0.2113, Mean Absolute Percentage Error (MAPE) of 0.5525%, and a Coefficient of Determination (R²) of 0.9681 for THI predictions. By combining low-cost, metrologically traceable IoT hardware with high-precision machine learning forecasting, this system provides a reliable and scalable framework to transition urban thermal risk management from reactive tracking to proactive climate adaptation.]]>
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