The spontaneous formation of order in complex systems remains a central problem across physics, biology, and artificial intelligence. While thermodynamics constrains irreversibility through entropy, it does not fully explain why organized structures emerge, persist, and adapt far from equilibrium. In this article, I develop an information-centered framework in which order formation is driven by informational constraints that channel system dynamics and counteract entropic dispersion. I analyze the mechanisms through which information shapes organization in natural and artificial systems, introduce a unifying interpretation based on organizational efficiency, and demonstrate how this perspective applies across physical, biological, and computational domains. This work provides a coherent theoretical foundation for understanding order as an information-driven phenomenon. Keywords Information-driven organization; order formation; complex systems; entropy; self-organization; biological systems; artificial intelligence; non-equilibrium thermodynamics.