Landslides are categorically the most impactful disasters on the planet and are the greatest contributing factors to the destruction of human life, infrastructure, and natural systems, especially in regions characterized by mountains and hills. The increase in the occurrence and severity of landslides associated with climate change forces an emphasis on the efficiency and effectiveness of detection means. This paper focuses its emphasis on the development of a comprehensive landslide detection system through the efficiency of integration available techniques. The model features the Faster Region-based Convolutional Neural Network to detect objects, U-Net for segmentation of images at high levels of detail, and Spatial Attention Gate networks to improve feature selection and importance to the context. The evaluation of the effectiveness of the proposed approach was based on measures of Intersection over Union and Dice Coefficient, and significant improvements were claimed in comparison to the traditional methods of detection. This research represents a major development in the field of landslide detection systems in that it equips World policy makers, together with emergency response units, with effective mechanisms to avert such risks to at-risk populations.