This work proposes an integrative framework entitled Theory of Anticipatory Biological Systems under Energy–Informational Coupling, aimed at understanding and modeling complex biological systems as dynamic entities capable of inference, adaptation, and self-regulation over time. From a multiscale perspective, the study articulates principles from biology, physics, chemistry, and mathematics to describe how cellular processes emerge from the interaction between metabolic state, energy availability, intracellular signaling, and molecular regulation. At the core of this proposal lies the notion that biological systems operate under architectures of inference and control in partially observable environments, in which cellular decisions—such as proliferation, differentiation, and stress response—are not deterministic outcomes but arise from continuous state updates driven by feedback mechanisms. Within this context, processes such as phosphorylation, telomere dynamics, gene regulatory circuits, and neuromodulatory activity are interpreted as coupling operators that link energy, information, and function. The theory introduces a conceptual structure analogous to a biological loss function, whereby the system tends to minimize internal and external imbalances, dynamically adjusting its trajectory toward regions of greater functional stability. This adaptive behavior is observable across multiple scales, ranging from local chemical reactions and molecular interactions to neural circuits responsible for real-time decision-making and communication. Furthermore, this work discusses the current limitations of modern science in achieving sustained, time-continuous control over such systems. While technologies such as gene editing, epigenetic modulation, and signaling control enable targeted interventions, there is still no unified framework capable of maintaining stable biological reprogramming over extended temporal scales. As a contribution, this framework establishes a theoretical basis for the development of computational and experimental models that represent biological systems as anticipatory entities governed by energy–informational coupling principles. Potential applications span regenerative medicine, immunometabolism, and neuroscience, offering a new paradigm for understanding and intervening in complex biological dynamics.