Understanding how species originate, diversify, and change across geographiclandscapes is a major topic in evolutionary biology. Therapid increase inphylogenetic and biogeographic datasets has driven need for integrativeanalytical platforms capable of reconstructing ancestral states whileaccommodating ambiguity. RASP (Reconstruct Ancestral State in Phylogenies)has developed as a frequently utilized tool for historical biogeography, enablingusers to apply likelihood-based models, Bayesian frameworks, and parsimonyapproaches within aunified graphical environment.RASP 4 includes considerabledevelopments, including generalized statistical integration across posterior trees,parallel computing capabilities, phylogenetic signal assessment, continuous anddiscrete trait evolution tools, and tree-comparison metrics.This paper synthesizesthe theoretical underpinning, methodological framework, and practicalfunctioning of RASP, stressing its implementation of BioGeoBEARS models (DEC,DIVALIKE, BAYAREALIKE), Bayesian Binary MCMC, and S-DIVA.Applicationsacross plants, animals, fungi, and vertebrates indicate its capacity to inferbiogeographic history, discover vicariance and dispersal mechanisms, and clarifyevolutionary patterns under difficult topography or ecological circumstances.Case studies with TurkishAstragalusandGundeliataxa indicate RASP`s usefulnessin places with significant endemism and environmental variation, while examplesfrom Malagasy lizards and fungal infections highlight its versatility in addressingphylogenetic and ecological concerns.Although RASP enablessophisticatedvisualization, model testing, and uncertainty integration, its output remainssubject to geographic coding accuracy and model assumptions. Nevertheless,expanding genomic datasets and developing evolutionary models assure thatRASP will continue to function as a crucial tool in biogeography, trait evolution,and macroevolutionary study.