In an era where AI systems are increasingly integrated into everyday life, clear and adaptive communication has become essential for effective collaboration. Traditional communication theories, such as Cognitive Load Theory and the Elaboration Likelihood Model, offer important insights into audience engagement but fall short in addressing the real-time interaction challenges presented by AI-mediated communication. This paper proposes the Theory of Explanatory Communication (ToEC) as a framework for fostering trust, understanding, and adaptability in human-AI interactions. Drawing from cognitive science, symbolic interactionism, and user-center ed feedback mechanisms, To EC emphasizes iterative adaptation and contextual relevance. By synthesizing concepts from Atkins ’ model of explanation attributes and emerging theories such as the Mutual Theory of Mind, ToECrepositions explanatory communication as a foundational human competence crucial for AI-mediated environments. This framework offers practical implications for AI design, public speaking, education, and healthcare, reinforcing the need for structured, adaptive communication in an increasingly complex technological landscape.