Slope landslides in open-pit coal mines critically threaten mining safety. Traditional early-warning systems face limitations, including data silos, false alarms, and an overemphasis on data collection over analysis. To overcome these challenges, this study develops a novel cloud-based Early Warning System (EWS) that integrates the Internet of Things (IoT) for multi-source data acquisition, Long Short-Term Memory (LSTM) networks for high-precision time-series prediction, and Dempster–Shafer (D-S) evidence theory for uncertainty-based multi-indicator fusion. Unlike conventional EWSs relying on single-threshold rules, the proposed framework performs real-time, data-driven risk reasoning, significantly reducing false alarms and enhancing system robustness under heterogeneous monitoring conditions. Field deployment at the Zhonglian Runshi open-pit mine demonstrated that the proposed LSTM model achieved a prediction accuracy of R² = 0.91 (RMSE < 0.11 mm), while maintaining a tenfold lower computational cost than Transformer-based models. Through multi-source integration of 69 sensors, the system achieved continuous, real-time risk assessment with reliable multi-channel alerts. These results confirm the feasibility of lightweight, cloud-enabled intelligent EWSs for large-scale open-pit mines, providing a scalable paradigm for intelligent geohazard monitoring and early warning.