The rapid integration of artificial intelligence (AI) into organizational processes has led to the rise of hybrid human–AI decision-making, where humans and intelligent systems collaborate to enhance decision quality, speed, and adaptability. Despite its growing adoption, theoretical foundations remain fragmented across fields such as cognitive science, information systems, machine learning, and organizational behavior. This paper proposes a unified theoretical model for hybrid decision systems by synthesizing Resource-Based View (RBV), Cognitive Load Theory (CLT), Socio-Technical Systems Theory (STS), and Human–AI Trust Calibration Theory. Through a conceptual framework and an analytical review, the study explains how hybrid intelligence develops, how decision responsibilities are distributed, and how trust and transparency influence outcomes. Further, a Hybrid Decision Interaction Model (HDIM) is proposed to explain dynamic relationships between human expertise, AI capabilities, organizational context, and task complexity. The paper contributes a comprehensive foundation for future empirical research and provides actionable insights for organizations implementing AI-assisted decision systems.