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The design of multicomponent alloys, including high-entropy and complex concentrated systems, remains constrained by the methodological limits of existing computational frameworks. CALPHAD provides a physically rigorous basis for thermodynamic prediction, yet its scalability deteriorates sharply with increasing compositional complexity. Machine learning offers complementary strengths in navigating high-dimensional spaces and extracting latent structure from sparse data, but its predictions frequently lack thermodynamic consistency. This work advances the position that neither paradigm is sufficient in isolation and that their integration constitutes a necessary evolution in computational alloy design. A unified hybrid framework is proposed in which machine learning augments CALPHAD through accelerated parameterization, while thermodynamic constraints are embedded directly within learning architectures to enforce physical validity. This integration extends to iterative, closed-loop workflows in which active learning coordinates prediction, equilibrium evaluation, and targeted data acquisition. Such coupling reconciles the extrapolative capacity of data-driven models with the interpretability and stability of thermodynamic formalisms, thereby addressing the characteristic failure modes of each approach. The argument is situated within the rapid expansion of multicomponent alloy research, where the combinatorial growth of composition spaces has exposed the limitations of standalone methods. By aligning statistical inference with thermodynamic structure, hybrid workflows enable scalable and physically grounded exploration of complex systems. Realizing this potential requires interoperable computational infrastructure, open thermodynamic data, and validation strategies that prioritize both predictive accuracy and consistency with phase equilibria. Under these conditions, hybrid ML–CALPHAD approaches establish a coherent pathway toward predictive, physics-informed alloy design.

High-entropy alloy design requires exploring an enormous composition space in which conventional trial-and-error approaches are no longer viable. Density functional theory (DFT) delivers accurate formation energies and phase-stability predictions yet remains computationally prohibitive, yielding only sparse datasets of a few hundred structures per study. By contrast, the CALPHAD method furnishes abundant thermodynamic data across millions of compositions in seconds, yet it carries systematic biases when extrapolated beyond its binary and ternary calibration regimes. This conceptual framework presents a multi-fidelity learning strategy that systematically fuses sparse high-fidelity DFT data with abundant low-fidelity CALPHAD predictions to achieve near-DFT accuracy at CALPHAD-scale coverage. The framework rests on four tightly coupled components: a data-integration module that aligns CALPHAD and DFT outputs on identical compositions, a bias-correction module that learns the systematic mapping between the two fidelities, an uncertainty-propagation module that decomposes and combines fidelity-specific uncertainties, and an active-learning module that strategically selects the next DFT calculations where correction is most needed. An operational protocol translates these components into a repeatable workflow that begins with broad CALPHAD screening, proceeds through iterative DFT calibration, and converges when uncertainty falls below a designer-specified threshold. The approach reduces the DFT budget by approximately two orders of magnitude while preserving predictive fidelity, thereby opening previously inaccessible regions of high-entropy alloy space. Beyond immediate efficiency gains, the framework establishes a reusable blueprint for hybrid computational materials engineering in any system where abundant low-fidelity models coexist with sparse high-fidelity benchmarks. It therefore offers both a practical design pipeline for high-entropy alloys and a generalizable conceptual scaffold for multi-fidelity learning in complex concentrated alloys.