As intelligent systems increasingly participate in critical decision-making across healthcare, finance, manufacturing, and public governance, the calibration of human trust toward these systems has become essential for safety, performance, and ethical reliability. Trust calibration refers to achieving an optimal level of trust—neither over-reliance nor under-reliance—so that humans interact with AI appropriately. This article synthesizes theoretical foundations from cognitive psychology, human–computer interaction, and AI ethics to propose a Trust Calibration Framework (TCF). It includes conceptual diagrams, error-risk models, human–machine trust curves, and an interpretability-driven trust cycle. The research identifies key psychological parameters, technological factors, and organizational structures influencing trust in AI systems. The paper concludes with practical strategies for implementing trust calibration in enterprises and recommendations for future research.