Detecting a landslide in prone areas has been a challenging task these days. Though the old methods and techniques like field surveys and visual representation are adopted, they seem time-consuming and costly and require many labors. Artificial Intelligence, Deep Learning, and Machine Learning have made a great impact and had a wider range of applications in many fields, which made the detection of landslides much easier. In this paper, we have introduced an accurate way detecting of landslides that are triggered due to the explosion of earthquakes. This advanced approach has four modules; they are uploading data, pre-processing the dataset, running the algorithm, and then predicting the occurrence. The Random Forest algorithm was carried out to increase the accuracy of this approach when compared to the previous works. We introduced more training samples or examples into the dataset for an accurate result to be obtained. Accurate landslide detection and mapping are essential for land use planning, management/assessment, and geo-disaster risk mitigation as well as post-disaster reconstructions. Till now, visual interpretation and field surveys are still the most widely adopted techniques for landslide mapping, which are often, time-consuming, and costly. With the rapid advancement of artificial intelligence, a deep learning-based approach for landslide detection and mapping has drawn great attention for its significant advantages over traditional techniques. This project aimed to examine the feasibility of an RF-based approach. With this approach of an Early Warning System for an Earthquake, we can easily identify the explosion or the warning to predict the landslides. We use the P-wave arrival time differences and the location of the seismic stations to locate the earthquake in a real-time way. Including a large number of training samples had made this approach more effective and the algorithms used had provided accurate results for an earthquake. This approach increases the accuracy of early detection further decreasing the chance of deaths.