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Landslides are a significant issue in India due to the country's varied topography, heavy monsoon rains, and deforestation, which contribute to soil instability and increased landslide risk. These natural disasters can cause damage to infrastructure and loss of life. In light of the ongoing problem of landslides in India, this research paper aims to address the need for effective landslide prediction strategies. Through the findings of this research study, a novel approach has been presented for predicting landslide occurrences in India, which will aid in reducing the impact of these events on infrastructure, communities, and lives. The work that has been carried out using data and information based in India has shown to have a low accuracy level. As a result, the model created using this information is not deemed to be very reliable. This study focuses on predicting landslides in India using machine learning models such as (Xtreme Gradient Boost) XGboost, random forest, and AdaBoost. Previous research on landslide prediction in India had not been widely done or had not achieved acceptable accuracy levels. This study aims to address this gap in knowledge and improve the predictability of landslides in India. The research is based on a database of different areas in India and aims to increase awareness and save lives and resources by predicting landslides with 91% accuracy. [ABSTRACT FROM AUTHOR]