Real-time monitoring of electrical appliances is a fundamental component of efficient energy management systems. Traditional Non-Intrusive Load Monitoring (NILM) techniques often rely on centralized cloud-based processing, which can introduce latency and bandwidth constraints. This paper presents a decentralized, Edge AI-based monitoring system implemented on an ESP32 microcontroller. By integrating a PZEM004T sensor for real-time electrical data acquisition, I developed a system capable of classifying appliance states locally. To improve model performance, I utilized feature engineering to derive power consumption (calculated as the product of voltage and current) alongside raw electrical parameters. A machine learning model was trained using the Edge Impulse platform, achieving 100% classification accuracy on a validation dataset. Furthermore, to address transient signal noise observed during load switching, I implemented a temporal filtering algorithm to ensure system stability and reduce false positive state transitions. My findings demonstrate the viability of deploying robust TinyML models for low-latency appliance monitoring, providing a cost-effective and privacy-conscious alternative to cloud-dependent energy management solutions.