Researchers at the National Institute of Technology Karnataka (NITK), Surathkal, have created SVALSA, an advanced landslide early warning system designed for the landslide-prone Western Ghats. This new framework combines rainfall data, real-time soil monitoring, and machine learning to give reliable warnings while reducing false alarms. Nearly 60% of Indian landslides happen in the Western Ghats, mostly triggered by heavy rainfall. Existing warnings mainly rely on rainfall, often missing soil condition changes and causing false alarms. SVALSA moves beyond by integrating hydrology, soil strength, and surface movement in one system. Over 90% of landslides here occur in weathered soils sensitive to moisture changes. Developed by Varun Menon and supervised by Sreevalsa Kolathayar, with support from DST, IMPRINT, and NTTM, the system uses a three-stage warning method. First, a machine learning model (K-Nearest Neighbour) analyzes rainfall patterns to reduce unnecessary alerts. Second, soil stability is checked using a soil mechanics method factoring moisture and suction. Third, surface movement is tracked via image analysis (Particle Image Velocimetry) to spot early warning signs. Tests confirm this combined approach boosts accuracy. The SVALSA device includes sensors and a compact processor to provide real-time alerts remotely to authorities. It is ideal for hill roads, railways, settlements on slopes, and infrastructure across the Western Ghats. Adoption could enhance disaster response, enabling timely evacuations and lowering risks to life and property.