<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" article-type="Research Article" dtd-version="1.0"><front><journal-meta><journal-id journal-id-type="pmc">iarjet</journal-id><journal-id journal-id-type="pubmed">IARJET</journal-id><journal-id journal-id-type="publisher">IARJET</journal-id><issn>2708-5163</issn></journal-meta><article-meta><article-id pub-id-type="doi">https://doi.org/10.47310/iarjet.2025.v06i01.003</article-id><title-group><article-title>Enhanced IoT-Based Environmental Monitoring System Using MQTT with Real-Time Alerts and Predictive Analytics</article-title></title-group><contrib-group><contrib contrib-type="author"><name><given-names>Mohammed</given-names><surname>M. Sultan</surname></name></contrib><xref ref-type="aff" rid="aff-a" /></contrib-group><aff-id id="aff-a">Mathematics Department, Education of Girls College, Tikrit University, Tikrit, Iraq</aff-id><abstract>We present an IoT environmental monitoring system that&amp;nbsp; uses three innovative features to improve traditional methods: ESP32-based sensor nodes with DHT22 sensors (30-second sampling,&amp;nbsp; ±0.5°C/±2% RH accuracy, JSON payloads), real-time alerting via MQTT (QoS 1 +&amp;nbsp;TLS) and Node-RED (SMS/email in &amp;lt;5s), and predictive analytics using linear regression&amp;nbsp; (85% accuracy for &amp;gt;2°C/5% RH trends). The Raspberry Pi-based system with Mosquito broker demonstrates 99.8% message reliability and 99.2% uptime during 30-days tests across residential place. Our solution costs less than $50 per node while using open-source tools (InfluxDB/SQLite) to achieve incident response time reduction from hours to seconds and risk forecasting accuracy of 20-40 minutes ahead of existing passive monitoring systems.</abstract></article-meta></front><body /><back /></article>