Landslides pose significant threats to human lives, infrastructure, and the environment, with the frequency and intensity of these events escalating in recent years. This escalation is primarily attributed to rapid urbanization, deforestation, and climate change, underscoring the urgent need for effective disaster risk reduction initiatives such as landslide vulnerability assessment. In response, this study presents a state-of-the-art approach to landslide vulnerability assessment by integrating the Analytic Hierarchy Process (AHP), Fuzzy Logic Model, and Geoinformation Technology. The integration of these methodologies offers a comprehensive framework for assessing landslide vulnerability, encompassing structured decision-making, uncertainty modeling, and spatial analysis. Geographic Information Systems (GIS) play a pivotal role in this process by enabling the integration, analysis, and visualization of spatial data layers, facilitating the identification of high-vulnerability areas prone to landslides. By applying the integrated AHP-Fuzzy Logic Model within a GIS framework, the study assesses the landslide vulnerability in the hilly districts of Bangladesh, pinpointing high-vulnerability zones. The AHP methodology is employed to establish the relative importance-based weight of 18 selected landslide susceptibility factors, guiding a structured decision-making process. Subsequently, the Fuzzy Logic Model handles the inherent uncertainty and vagueness associated with landslide vulnerability assessment, utilizing fuzzy linear membership functions for the fuzzification of contributing factors and employing a fuzzy gamma overlay technique for landslide vulnerability modeling. This integration of fuzzy logic allows for more realistic modeling of complex systems, capturing the ambiguity and imprecision inherent in landslide susceptibility factors. The outcomes of the study reveal that about 68% of the total area is significantly vulnerable to high and very high levels of landslides, with Khagrachari and Rangamati districts identified as the most vulnerable primarily due to the substantial extent of very high-vulnerability areas within their territories. Furthermore, the predicted landslide vulnerability is validated using Receiver Operating Characteristic curves (ROCs) and the Area Under the Curve (AUC), achieving a reliability of 90.34%. These findings contribute to the development of effective landslide risk management strategies not only in the hilly regions of Bangladesh but also in other landslide-prone areas globally. By leveraging the synergies between AHP, Fuzzy Logic Model, and Geoinformation Technology, stakeholders can make informed decisions to mitigate the impacts of landslides and enhance the resilience of vulnerable communities. This interdisciplinary approach holds promise for addressing the complex challenges posed by landslide hazards and advancing sustainable disaster risk reduction efforts.