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Polymer blend systems occupy a central position in soft matter materials science, where macroscopic properties emerge from complex, multi-scale interactions among molecular architecture, phase morphology, and processing history. Although artificial intelligence (AI) is increasingly used to predict properties of polymer blends, most existing approaches prioritize prediction accuracy over interpretability, limiting their contribution to theoretical understanding and rational materials design. This paper introduces a purely conceptual framework for explainable artificial intelligence (XAI)–enabled structure–property mapping in polymer blend systems, positioning interpretability as a foundational epistemic requirement rather than a post-hoc diagnostic. Given the recent advances in machine learning, polymer informatics, and explainable AI, the framework conceptualizes structure–property maps as interpretable landscapes where predictions, feature attributions, and uncertainty coexist as integrated elements. By explicitly incorporating multi-scale descriptors—from molecular chemistry to mesoscopic morphology—and embedding XAI mechanisms such as feature attribution and counterfactual reasoning, the proposed framework aligns data-based insights with established principles of soft matter physics and thermodynamics. Instead of advanced algorithms or empirical models, this work articulates a theoretical architecture that shows how AI-derived representations can support causal reasoning, trade-off analysis, and epistemic restraint in the design of the polymer blend. Ultimately, the paper provides a framework-level perspective on the application of AI in materials science and defends the structure–property mapping approaches, in which interpretability, physical grounding, and uncertainty awareness are central to scientific meaning and responsible materials innovation.