Landslides are among the most devastating natural disasters worldwide, causing significant loss of life and infrastructure damage in hilly and mountainous regions. This paper presents the design and implementation of a low-cost, real-time landslide detection and early warning system based on the Internet of Things (IoT) framework. The proposed system integrates multiple heterogeneous sensors — including soil moisture sensors, vibration sensors (piezoelectric), tilt sensors, rain gauges, and temperature-humidity modules deployed across vulnerable slopes. Sensor data is collected by microcontroller nodes (ESP32/Arduino) and transmitted wirelessly via LoRaWAN or Wi-Fi to a cloud-based IoT platform (ThingSpeak/Blynk). A threshold-based machine learning algorithm processes incoming sensor data to classify risk levels (Normal, Warning, Critical) and triggers automated SMS/email alerts and visual alarms. Experimental results from a prototype deployment demonstrate a detection accuracy of 94.7%, alert latency below 2 seconds, and system uptime exceeding 98% over a 30-day evaluation period. The proposed system offers a scalable, affordable, and energy-efficient solution for real-time landslide monitoring in remote and resourceconstrained environments.