Identifying suitable habitats and High Conservation Value Areas (HCVAs) for threatened species is critical for informing site-based management in biodiversity-rich, but vulnerable landscapes. In this study, Species Distribution Modelling (SDM) and species richness analysis were applied to identify areas of high conservation value within Rajah Sikatuna Protected Landscape (RSPL). Using the MaxEnt algorithm, we modelled habitat suitability for thirteen threatened species across major taxonomic groups, incorporating topographic and bioclimatic variables. Models showed high performance (mean Area Under Curve (AUC) = 0.905, True Skill Statistic (TSS) = 0.717 and Variance Inflation Factor (VIF) < 4), with terrain ruggedness, slope, mean annual temperature and annual precipitation identified as primary drivers of model structure. While statistical performance was robust, model outputs were strictly framed as spatial hypotheses to account for the data-limited nature of the focal endemics and the use of bioclimatic layers as proxy gradients. The resulting potential suitability maps revealed heterogeneous, species-specific distributions, ranging from localised and fragmented habitats for Dipterocarpus grandiflorus , Gallicolumba crinigera and Shorea guiso , to broader ranges for Edolisoma mindanense , Platymantis guentheri and Voacanga megacarpa . Stacked binary models produced a species richness map that highlighted northern and north-western RSPL as biodiversity hotspots, while southern areas were relatively depauperate, reflecting probable degradation. Threshold-based analyses to delineate HCVAs showed that the CRFD (Cumulative Richness Frequency Distribution) inflection point (~ 6 species) best balanced spatial extent and ecological sensitivity, capturing 90% of species occurrences. In contrast, conservative thresholds (e.g. 95 th percentile, Mean + 1 SD) isolated only a few core hotspots, but excluded moderately rich areas that may serve as corridors or buffer zones. This integrated modelling approach offers valuable insights, enabling the prioritisation of potential critical habitats and identification of targets for ecological restoration.