Earthquakes pose significant challenges to disaster management due to their sudden and catastrophic nature. Traditional seismic monitoring systems, while effective, often face limitations in scalability and accessibility, particularly in remote regions. This paper presents QuakeSense, an IoT-based edge earthquake detection system that addresses these issues by integrating machine learning, edge computing, and IoT infrastructure. The system, built around the Arduino Nano RP2040 Connect, uses high-precision accelerometers and gyroscopes to detect seismic activity in real time. Decision Tree (DT) model, selected for its compact size (8.46 KB) and impressive accuracy (99.94%), enables fast, efficient earthquake detection on edge devices. The system triggers local alerts and sends real-time data to the ThingSpeak platform for further monitoring and email notifications. With its decentralized architecture, QuakeSense reduces latency, improves scalability, and delivers reliable earthquake monitoring, offering an innovative solution for mitigating seismic risks.