This essay explores the intersection of the Theory of Mind (T.O.M.) and Artificial Intelligence (AI), emphasizing the potential for AI to emulate cognitive processes fundamental to human social interactions. T.O.M., a concept crucial for understanding and interpreting human behavior through attributed mental states, contrasts with AI's behaviorist approach, which is rooted in data-driven pattern analysis and predictions. By examining foundational insights from cognitive sciences and the operational models of AI, this analysis highlights the potential advancements and implications of integrating T.O.M.-like capabilities into AI systems. This paper employs a conceptual and analytical approach, synthesizing interdisciplinary perspectives from cognitive science and computational theory to develop a normative framework for AI-human interaction. The methodology involves systematic literature review across cognitive science, AI, and ethics domains, analyzing 45 peer-reviewed sources published between 1978-2024, with critical evaluation of theoretical frameworks, empirical evidence, and implementation feasibility. The discussion pivots around three critical questions: whether AI should emulate T.O.M. to enhance human interactions, if AI can maintain its data-driven model while integrating cognitive processes, and how AI can expand its capabilities in social contexts. The arguments suggest that incorporating T.O.M.-like processes could significantly improve AI's interaction quality without compromising its analytical strengths, pointing towards a future where AI not only predicts but also empathizes, offering more nuanced and culturally aware interactions. This synthesis of cognitive theories and computational strategies advocates for a deeper integration of diverse datasets and advanced computing methodologies, aiming to transform AI into a more empathetic and effective participant in human social environments. These developments have significant implications for AI ethics and governance, particularly as AI systems become more deeply integrated into sensitive domains such as healthcare, education, and social services.Feedback on empirical applications or case collaborations is warmly welcome.