The research objective is to formalize the cognitive stratified framework of digital fiscal control through the unification of information and analytical tools based on decomposition, topological analysis, and Unified Modelling Language (UML). The study employed the following methods: SWOT analysis of solutions for the digitalization of fiscal tax control systems, decomposition and range analysis of digitalization technologies, topological analysis of digital information and analytical tools, and UML modelling of the framework for the digitalization of fiscal tax control systems. The developed framework is presented as a conceptual and architectural design structure that integrates artificial intelligence (AI)/machine learning (ML) risk stratification, Distributed Ledger Technology (DLT) traceability, autonomous compliance, and P2P interoperability to outline architectural integrity and procedural resilience as intended design properties rather than empirically demonstrated effects. SWOT, decomposition and range analysis, as well as topological analysis supported the identification of a unitary routing logic for risk, compliance, and verification flows that may contribute to fiscal transparency and evasion risk mitigation under subsequent pilot testing. The academic novelty is associated with the systemic identification, decomposition, structured organization, and topological mapping of information-analytical tools of fiscal control, which enabled the formal representation of a cognitively stratified architectural and functional topology of a digital fiscal control framework using UML modelling.