Landslides pose significant risks to life, property, and infrastructure, necessitating effective monitoring and early warning systems. Traditional methods relying on manual observations and groundbased sensors often fall short in providing timely and comprehensive data. This research introduces an innovative IoT-based system for real-time landslide prediction and monitoring. The proposed system employs a network of advanced sensors, including geophones, inclinometers, rain gauges, and soil moisture sensors, to continuously collect data on critical environmental parameters such as soil movement, groundwater levels, rainfall intensity, and soil moisture content. Edge devices at sensor nodes process and filter the data locally, minimizing transmission loads and enhancing real-time responsiveness. Machine learning algorithms analyze this data to detect patterns indicative of impending landslides, enabling the development of predictive models that trigger early warnings. The system leverages cloud computing for large-scale data storage and analytics, facilitating detailed trend analysis and pattern recognition. Additionally, it integrates geographical information systems (GIS) for dynamic visualization of landslideprone areas and real-time monitoring, supporting proactive risk assessment and mitigation strategies. This IoT-based approach aims to revolutionize landslide monitoring by providing accurate, real-time data and predictive insights, ultimately enhancing community resilience and disaster preparedness.