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Landslides pose significant threats to infrastructure and communities in the Republic of Moldova, yet until now no comprehensive national-scale inventory or susceptibility assessment has been available. This study presents the first complete landslide inventory and AI-based susceptibility model for the entire country, integrating multi-source remote sensing data with presence-only machine learning techniques.We developed a new landslide inventory comprising 246 polygons through visual interpretation of aerial imagery, orthophotos, and LiDAR data (5m resolution in central regions), complemented by field verification. This inventory was integrated with existing databases to create a comprehensive dataset of 1,523 landslide polygons for susceptibility modeling. Landslides were classified following international schemes, focusing on slide- and flow-type movements in medium- to deep-seated failures, while excluding shallow landslides, rockfalls, and debris flows.Susceptibility analysis employed the MaxEnt presence-only machine learning algorithm with environmental variables including slope, elevation, valley depth, topographic wetness index, normalized height, Gaussian and Casorati curvature, lithology, and land cover derived from 30m resolution JAXA DEM and 1:200,000 geological maps. The model demonstrates strong predictive performance, with 68% of mapped landslides exhibiting mean susceptibility values exceeding 0.7.Results reveal distinct spatial patterns: high-susceptibility zones (susceptibility values 0.7-0.997) form continuous corridors along valley networks in the central and northern hilly regions (Codrii Hills, Ciuluc Plateau, Dniester Hills), while southern and northern plains exhibit consistently low susceptibility (~8.27×10⁻¹¹ to 0.3). Geomorphometric analysis shows landslides preferentially occur at mid-slope positions (normalized height 0.3-0.6), in areas with moderate valley depths (15-28m median), and intermediate topographic wetness index values (7-10), reflecting strong structural control by cuesta landforms and Miocene clay-rich lithologies.The bimodal distribution of susceptibility values within the inventory, with peaks at both low (0.8) values, suggests the presence of both active landslides under current environmental conditions and relict features formed during wetter Pleistocene climates. This interpretation aligns with regional studies from adjacent Romanian territories.This research provides the first national-scale susceptibility map for Moldova and establishes a scalable framework for landslide risk assessment in regions with heterogeneous geomorphology and incomplete historical data. The results support strategic planning for hazard mitigation, infrastructure development, and land-use management, particularly in densely populated agricultural regions where landslide impacts are already documented. Future work should focus on incorporating temporal triggering factors, anthropogenic influences, and climate change scenarios to enhance predictive capabilities.Acknowledgements: This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS – UEFISCDI, project number 40PCBROMD within PNCDI IV.