With the recent increase in the number of earthquakes in Korea, research efforts have been directed toward the real-time detection of earthquakes and the formulation of evacuation plans. Traditional seismometers can precisely record earthquakes but are incapable of processing them on-site to initiate an alert and response mechanism. By contrast, internet of things (IoT) devices equipped with accelerometers and CPUs can record and detect earthquake signals in real time and send out alert messages to nearby users. However, the signals recorded on IoT devices are noisy because of two main factors: the urban buildings and structures these devices are installed in and their cost-quality trade-off. Therefore, in this work, we provide an effective mechanism to deal with the problem of false alarms in IoT devices. We test our previously proposed artificial neural network (ANN) with different feature window sizes ranging from 2 seconds to 6 seconds and with various earthquake intensities. We find that setting the size of the feature window to a certain interval (i.e., 4-5 seconds) can improve model performance. Moreover, an evacuation route guidance platform that considers user location is proposed. The proposed platform provides and visualizes information to user devices in real time through the communication between server and user devices. In the event of a disaster, safe shelters are selected on the basis of the information entered from the server, and pedestrian paths are provided. As a result, the direct and secondary damages caused by earthquakes can be avoided.