Ixodes ricinus is the main hard-tick vector of numerous pathogens across Europe, making its fine-scale distribution a key determinant of human and animal exposure risk. Existing habitat-suitability and distribution maps have already provided important knowledge on I. ricinus in France and Europe, including knowledge- based national mapping and European-scale species distribution models. Here, we present a complementary, occurrence-based machine-learning framework designed to produce an ecologically interpretable habitat- suitability map for mainland France, with predictor selection explicitly grounded in a PRISMA-guided synthesis of the literature. We systematically reviewed the European literature to identify climatic, environmental, and biological drivers of tick survival, development, and questing activity. These drivers were translated into spatial layers and filtered for collinearity, ecological relevance, and interpretability. Occurrence records from citizen- science and institutional sources were manually curated for taxonomic reliability and geographic precision. Pseudo-absences were generated using constrained strategies accounting for sampling bias and accessible-area geometry. Models were trained and evaluated using environmentally stratified cross-validation to limit spatial autocorrelation, and algorithms were benchmarked with optimized hyperparameters using discrimination metrics and ecological diagnostics. The resulting suitability maps revealed a strong national gradient primarily associated with land cover, saturation deficit, and intra-annual humidity variability. Suitability was highest in contiguous forested landscapes and lower in urbanized areas and regions characterized by high saturation deficit, including zones under Mediterranean climatic influence. The categorical map delineated coherent high- suitability cores separated by broad transition zones, consistent with known eco-physiological limits and habitat requirements of I. ricinus. Response curves highlighted sensitivity to desiccation-related variables and favourable conditions in forests and peri-urban wooded environments, supporting biological plausibility. Rather than aiming to replace existing maps, this framework contributes a transparent and reproducible data-driven approach for deriving a national relative suitability surface and critically evaluating inferred ecological relationships against published evidence. The resulting map should therefore be interpreted as an index of relative habitat suitability, not as a direct measure of tick abundance or infection risk. It may support targeted surveillance, guide field sampling strategies, and provide a spatial component for future integration with pathogen and host-distribution layers to derive relative exposure indices. The approach is transferable to other tick species or regions where heterogeneous occurrence data and literature-supported ecological predictors are available.