<![CDATA[Explainable Artificial Intelligence (XAI) has become increasingly important in Engineering Decision Support Systems because growing algorithmic complexity often reduces transparency, accountability, and practitioner confidence despite substantial improvements in predictive capability. This study develops a conceptual framework that integrates engineering data processing, artificial intelligence inference, explainability mechanisms, and human-centered decision support within a unified multilayer architecture. A non-empirical system design methodology based on design-oriented systems research, architecture-based evaluation, and scenario-driven analytical simulation is employed to examine architectural consistency, interoperability, traceability, and explainability across representative engineering contexts. The analytical results indicate that explanation fidelity, interpretability, transparency, traceability, and modular scalability function as complementary engineering quality attributes that collectively strengthen trustworthy decision support while preserving logical consistency between engineering evidence and computational reasoning. The proposed architecture also demonstrates technology independence and adaptability across heterogeneous engineering domains through explicit modular interactions and standardized information flows. This study contributes an architecture-centered perspective that advances theoretical understanding of explainable engineering intelligence while providing a reproducible conceptual foundation for future empirical implementation, quantitative validation, and standardized evaluation of trustworthy Engineering Decision Support Systems. ]]>
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