Landslides are widespread disasters in hilly regions. These disasters cause lots of injuries and deaths every year. Due to these injuries and deaths, it is imperative to monitor landslides and to warn people about impending disasters timely. It is also important to predict soil movements ahead of time so that people get enough lead time to evacuate from the sliding region. The existing technologies monitor landslides at a very high cost and do not warn people and predict soil movements ahead of time. The primary objective of this paper is to detail the development, deployment, and evaluation of a new low-cost IoT-based landslide monitoring, warning, and prediction system. In this research, we developed and deployed a new system sub-surface, which is capable of generating real-time warnings via SMSes in case of significant sub-surface movements. We also performed predictive analytics on the data collected from the system using auto regression and sequential minimal optimization machine-learning models. The main advantage of the proposed system is that it is low-cost, and it works on the same principle of movement detection as that used by existing technologies. The lowcost makes this system mass deployable at many locations to monitor the landslides and to timely warn people. We discuss the implications of deploying this system at several landslide-prone locations in the world.