This paper presents the design and implementation of an autonomous IoT-enabled robotic vehicle developed for continuous environmental monitoring and predictive analysis. The system is built around an ESP32 microcontroller and integrates multiple sensors to measure temperature, humidity, air quality, gas leakage, smoke, alcohol vapours, vibration, motion, and obstacle distance. The vehicle is capable of autonomous navigation using ultrasonic-based obstacle avoidance while simultaneously collecting environmental data from diverse locations. The acquired sensor data is transmitted to a cloud platform in real time for visualization, storage, and analysis. To enhance system intelligence, machine learning techniques are employed to analyze historical and real- time sensor data for anomaly detection and future trend prediction. A live video streaming module based on ESP32-CAM is incorporated to provide real-time visual feedback, thereby improving situational awareness and operational safety in remote or hazardous environments. The proposed system offers a low-cost, scalable, and flexible solution suitable for applications such as smart agriculture, industrial safety monitoring, pollution surveillance, and disaster-prone area assessment.

This paper presented an IoT-based autonomous environmental monitoring vehicle that integrates multi- sensor data acquisition, cloud connectivity, live video streaming, and machine learning-based analysis. The system provides a reliable and scalable solution for real- time environmental monitoring in diverse applications.

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