Locust invasion issue is considered as one of food insecurity reasons around the world including Africa, Asia and Middle East since antiquity. Huge numbers of desert locusts are swarming across the affected regions and increase under breeding conditions. It can affect the health and the lives of millions of people. Different ways have been used to reduce the effect of locust invasion, chemicals to prevent swarm formation, satellites, and sensors to detect and monitor locust breeding areas. But these methods have limitations since they have not been able to put down the upgrowth and the mass behavior of locusts. In this study, we utilize machine learning to predict the location and density of locust swarms in advance using the available data published by the Food and Agriculture Organization of the United Nations and published on the Locust hub portal. The data consist of the location of the observed swarms and environmental information, including soil moisture and the density of vegetation. The results show that our model can predict the location of locust swarms and the expected level of damage using the density notion successfully.