<p>This work investigated the mechanical properties and machinability of the origami-inspired 3D-printed PA6-PLA polymer blends using advanced optimization algorithms, namely RAO-II, TLBO (teaching-learning-based optimization), JAYA (a parameter-less optimization algorithm), and the conventional TOPSIS (technique for order preference by similarity to ideal solution). The primary objective was to simultaneously enhance the mechanical performance and abrasive water jet machining (AWJM) by optimizing critical process parameters, such as jet pressure, standoff distance, and traverse speed, for these composite structures. Experimental results were analyzed through a multi-objective optimization framework, targeting surface roughness (SR), material removal rate (MRR), and kerf width (KW). Among the algorithms tested, the RAO-II algorithm produced the most favorable machining metrics, achieving an 18% reduction in SR, a 25% increase in MRR, and a 22% reduction in KW relative to baseline conditions. TLBO and JAYA also led to notable improvements but were inferior to RAO-II in overall performance. Although TOPSIS proved to be a feasible alternative, it was less successful in minimizing KW. These findings highlighted the superiority of the RAO-II method for optimizing AWJM of PA6/PLA polymer blends, making it particularly suitable for applications requiring high precision and efficiency. This work contributed to the advancement of the optimized machining processes for polymer blend materials, showcasing the potential of algorithm-driven approaches.</p> Graphical Abstract <p></p>