Effective communication is increasingly recognized as the critical determinant of success in complex project environments. Yet, despite decades of empirical research, communication, documentation, and knowledge management remain fragmented constructs within project management theory. This study proposes the Communicative Infrastructure Model CIM, a conceptual and mathematical framework that reconceptualizes project communication as a dynamic control system. The CIM integrates documentation usability D, knowledge integration K, risk communication efficiency R, and communication network quality CQ into a unified model based on systems theory, information entropy, and control stability analysis. Using a mathematical foundation derived from Shannon’s information theory and Lyapunov stability criteria, the model defines a Communicative Infrastructure Function CIF that quantifies how communication reduces uncertainty, mitigates risk, and stabilizes decision-making processes. The proposed framework positions communication not as a soft skill but as a measurable, strategic infrastructure that governs project equilibrium. By demonstrating that project systems achieve stability when communication efficiency exceeds a calculable threshold, this study establishes formal criteria linking communicative quality to project performance outcomes. A comparative analysis of recent studies (2020–2025) illustrates how existing empirical models of Agile collaboration, cross-functional coordination, and documentation practices can be mathematically integrated within the CIM. This theoretical advancement offers both researchers and practitioners a quantitative foundation for assessing and optimizing communicative effectiveness in project environments. It opens pathways for future empirical calibration through data-driven simulations, AI-enabled communication analytics, and real-time performance monitoring. The CIM thus redefines project communication as a cybernetic infrastructure of project management, capable of transforming risk, uncertainty, and knowledge flow into measurable system stability.