AI-powered IoT plant health monitoring system integrating DHT22 atmospheric sensors, soil moisture probes, and ESP32-CAM image capture across healthy and unhealthy crop sections. Data flows from edge sensors through an ESP32 microcontroller via Wi-Fi/MQTT to a cloud API endpoint, where an ML disease detection model processes images and sensor telemetry against a plant disease model registry. Processed insights stream to Blynk IoT dashboards (mobile and web) and monitoring logs, enabling real-time crop health alerts and intervention decisions. Fork this architecture to customize sensor types, swap cloud providers, or integrate alternative ML frameworks for precision agriculture deployments. The dual-section design (healthy vs. unhealthy) demonstrates comparative analysis for training and validating disease detection models in production environments.