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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.