The relevance of the study is determined by the need for high-performance data processing architectures to increase the efficiency of human resource management (HR) in the context of digital transformation and the increasing complexity of analytical processes. The aim of the research is to create an optimized data-driven human resource (HR) decision-making architecture with the integration of machine learning (ML) modules, adaptive pipelines, and streaming analytics, verified by Unified Modelling Language (UML) and metric comparison. Research methods: SWOT analysis, metric comparison, structural optimization decomposition, UML, comparative analysis of UML models, metric comparison of optimized architecture. Optimized HR Data-Driven Decision-Making architecture provided an increase in predictive accuracy to 0.962 (+8.4%), metric stability (+9.1%), and discriminability (+7.8%). SWOT analysis identified 6 strengths, 5 critical weaknesses, 4 risks; comparison confirmed the superiority of SAP Analytics Cloud (0.94; 0.91; 0.89) over the market average (0.81; 0.78; 0.75). UML integration of adaptive feature engineering pipelines, cognitively optimized ML modules, and streaming analytics increased interoperability (+14.6%), modular integrity (+12.3%) and algorithmic resilience (+15.1%). The academic novelty of the study is the formalization and verification of an optimized HR Data-Driven Decision-Making architecture that integrates ML modules, feature engineering, and stream analytics, providing a predictive accuracy of 0.962 (+8.4%) and interoperability of +14.6%. Prospects for further research include localized implementation of the architecture in a test environment with metric and cognitive functional verification, iterative optimization of modules based on the results of institutional contextual empirical testing.