Induced pluripotent stem cell (iPSC)-derived natu ral killer (NK) cells offer a promising, scalable platform for next generation immunotherapy manufacturing. However, variability in differentiation efficiency across biological batches makes early quality assessment challenging. Because NK maturation requires several weeks, failures detected at late stages can lead to substan tial losses of time, cost, and experimental resources. To address this, we propose a deep learning (DL) framework for automated, early-stage, and non-destructive assessment of NK maturation status using bright-field microscopy images of Day-10 cultures. Our approach leverages a domain-specific preprocessing pipeline and modern DL architectures to capture subtle morphological indicators of hematopoietic commitment. The best-performing model (ResNet18) achieved an accuracy of 96.25%, with t-SNE and Grad-CAM analyses confirming that the network learns biologically relevant structural patterns. By providing early predictive insights through a label-free workflow, this framework offers a scalable strategy to standardize quality control in iPSC NK cell manufacturing.