<p>The existing techniques in engineering face significant challenges in managing uncertainty and reliability in design optimization, which potentially leads to inaccurate outcomes in complex decision-making scenarios. In this article, we introduce a novel approach to multi-attribute decision-making (MADM) by leveraging intuitionistic fuzzy Z-numbers (IFZNs) and a newly proposed aggregation operator termed as generalized intuitionistic fuzzy Z-numbers weighted averaging (GIFZNWA) operator. IFZNs provide a robust framework for managing both uncertainty and data reliability in decision-making, which makes them especially suited for complex engineering optimization problems. The proposed GIFZNWA operator extends traditional fuzzy aggregation methods by incorporating a dual-layer representation of uncertainty and hesitancy, enabling more informed and accurate decisions. Moreover, a MADM algorithm is proposed based on IFZNs and the proposed GIFZNWA operator. The efficacy of this approach is demonstrated through its application in engineering design optimization in the aerospace industry. In particular, four turbine blade design alternatives are evaluated against multiple criteria such as weight, thermal resistance, mechanical strength, and manufacturing feasibility. The results of the study indicate that the High-Temperature Alloy Design emerges as the most suitable and preferred alternative. By integrating machine learning, we systematically improve cost estimation in turbine blade manufacturing. A sensitivity analysis confirms the stability of rankings, while comparative analysis with some existing decision-making techniques shows that this operator yields reliable and consistent results with the compared methods. Overall, this research contributes to the advancement of both fuzzy logic applications and decision-making methodologies in complex engineering and aerospace applications.</p>