This paper introduces a new Disruptive Event Dataset, DisEvD, for developing machine learning models to predict multiple disruptive events that can help to monitor and mitigate such events with appropriate actions. Earlier datasets did not include all the event categories that might lead to disruption, like crime, disaster, cyber-attacks, rumours, and crime-based events. Some of these datasets suffer from data scarcity, while others suffer low coverage. There is no dataset consisting of disruptive events tweets of the Indian subcontinent. Also, no disruptive event dataset contains geotagged tweets for identifying the location of the occurred event. Our dataset DisEvD contains all the major disruptive events that happened in the bounding box of India from 1st April 2022 to 15th Jan 2023. It best describes disruptive events with the help of geotagged tweets. The experimental results, using different clustering algorithms, show the effectiveness and usefulness of the dataset. Further, we also present the methodology to extract the disruptive event information from the events cluster.