<p>This study investigates the influence of cutting parameters and lubrication mode on the turning performance of 17-4PH stainless steel produced by Laser Powder Bed Fusion (LPBF). A mixed-level Taguchi L18 orthogonal array was employed to evaluate the effects of depth of cut, feed rate, cutting speed, and lubrication (dry and MQL) on surface roughness (Ra), cutting force (F), and material removal rate (MRR). Single-response optimization based on signal-to-noise (S/N) ratios revealed conflicting optimal conditions among the responses, highlighting the limitations of conventional Taguchi approaches for multi-objective problems. To overcome this limitation, Grey Relational Analysis (GRA) combined with Principal Component Analysis (PCA) was applied to transform the multi-response problem into a single weighted performance index (W-GRG). The weighting coefficients were derived objectively from PCA, ensuring a data-driven and statistically consistent optimization framework. Surface characterization showed that as-built LPBF surfaces exhibit high roughness and irregular topography due to partially melted particles and layer-wise deposition. These features significantly influence machining behavior and can be effectively reduced through finishing operations. The results indicate that feed rate predominantly affects surface roughness, while depth of cut governs both cutting force and overall machining performance. The influence of MQL remains limited compared to geometrical parameters, mainly contributing to improved cutting stability and surface integrity. Multi-response optimization identified the optimal parameter combination that provides a balanced compromise between surface quality, cutting load, and productivity. The proposed PCA–GRA methodology offers a robust and systematic framework for multi-criteria optimization and provides deeper insights into the specific machining behavior of LPBF materials, thereby enhancing the industrial applicability of additively manufactured 17-4PH components.</p>