Human–Data Interaction (HDI) has emerged as a critical framework for understanding how individuals engage with data-intensive digital environments. Cognitive Load Theory (CLT), rooted in instructional design and human cognition, provides explanations for how mental capacity is consumed during complex information processing tasks. This paper examines the intersection of HDI and CLT, proposing that HDI systems must be fundamentally re-engineered to minimize extraneous cognitive load and enhance user performance. Through conceptual modeling, supported by literature review and a synthesis of cognitive science principles, the paper introduces an Integrated Human–Data Cognitive Interaction (IHDCI) Framework. This framework highlights how data presentation, user autonomy, transparency, and cognitive load interact to shape decision-making. The analysis demonstrates that applying CLT principles—such as segmenting information, reducing redundancy, and optimizing modality—significantly improves users’ ability to interpret, manipulate, and trust complex datasets. The study concludes with implications for interface designers, data scientists, educators, and policy-makers.