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The tailings dam, a necessary facility to maintain the normal operation of mining enterprises, is a hazard source of human-caused debris flow with high potential energy. The real-time pre-alarm for the instability of tailings dam is vital to ensure the normal mining and safety of human lives and properties. Based on the Internet of Things and wireless networks, the multiple and the key information system of tailings dam is constructed using the sensor data, which include the stability indexes like phreatic line, reservoir water level, internal and external deformation of the tailings dam. The cloud platform is applied to predict the future state of the phreatic line based on real-time monitoring data, where the equation of phreatic line can be obtained. The numerical simulation model is established by considering the predicted equation of phreatic line, limit equilibrium state parameters, reservoir water level, and rainfall. Then, the safety factor, random reliability, and interval non-probabilistic reliability can be solved out through the cloud platform. Combined with the trend of real-time monitoring deformation, as well as calculated dynamic safety factor, random reliability, and interval non-probabilistic reliability, the stable or dangerous warning signals of tailings dam can be obtained by the remote real-time pre-alarm system. The main solved method for the key parameters and pre-alarm process are presented through a case study. It is proved that the pre-alarm system is an efficient and real-time platform for the tailings dam stability with the integration and mutual validation of key information.