<p>Rainfall-triggered landslides pose significant risks in hilly and mountainous areas, necessitating advanced susceptibility and hazard modeling in GIS environments. This review examines research trends, influential authors, and emerging themes in this field through bibliometric analysis and content review, covering publications from 2007 to 2025 with a focus on the last five years. Recent research trends reveal a shift towards complex, physically based models incorporating multiple rainfall variables and their interactions with local conditions. Emerging trends include machine learning applications, increased spatiotemporal analysis, and rising interest in climate change impacts on landslide occurrence. However, there is a lack of attention given to how topography and other factors might explain spatial differences in rainfall. Research output has increased since 2020, driven by initiatives like the Kyoto Landslide Commitment 2020. While progress has been made in landslide susceptibility modeling, several challenges persist, such as developing standardized protocols for case study research to enhance comparability; exploring methods to integrate multiple case studies and improve generalizability; investigating emerging technologies like big data analytics to address sample limitations; improving translation of research findings for practitioners and policymakers; and enhancing methods to incorporate topographic effects on rainfall variability in susceptibility models. Many studies rely on single-site case studies, limiting result generalizability. Better integration of topographic influences on rainfall spatial variability is needed, particularly in mountainous regions. Addressing these challenges will be crucial for advancing landslide risk assessment. Future research should focus on developing robust, transferable models that account for complex environmental interactions and provide practical decision-support tools.</p>