Traditional housing assessment methods rely on manual, retrospective reporting, which is prone to subjectivity and logging errors. In addition, conventional remote sensing systems focus mainly on passive microclimate logging without administrative integration. To overcome these limitations, we developed an IoT sensor-driven autonomous monitoring and evaluation system that links continuous sensor data with institutional decision-making. Heterogeneous sensor arrays in the system are used to capture gas concentrations, ambient microclimates, and electrical current waveforms at edge sampling frequencies up to 10 Hz. Streamed data are processed through an asynchronous, multithreaded producer–consumer pipeline and evaluated using a hybrid unsupervised anomaly detection framework that fuses isolation forest and the local outlier factor using a conservative ANDlogic strategy. In contrast to previous systems that produce raw anomaly flags or subjective reports, the developed system converts multisource sensor streams into objective, rule-capped housing scores explicitly designed for governance integration. Validation across 185 housing units (53000 hourly records) showed that the hybrid detector achieved a 96.8% precision, a 1.2% false positive rate, and an inference latency of 0.85 ms/sample, outperforming single-model baselines. Data processing time was reduced from 180 to less than 2 min, with 90 times speedup with a 0% computational error. The results of this research show that by coupling multirate hardware acquisition, noise-resilient dual-voting analytics, and explainable behavioral mapping, a governance-ready framework can be constructed, differing from previous anomaly detection systems that are limited to technical validation or isolated sensor deployments.