Natural killer (NK) cells are critical in anti-cancer immunity. However, the complex network of transcription factors (TFs) that govern their functional potency is not fully understood. This study aimed to identify novel regulators of NK cell function using single-cell transcriptomics and cutting-edge in silico analytic pipelines. Peripheral blood NK cells were isolated from 10 healthy donors and single-cell RNA sequencing was performed at both resting state and following a five-hour activation against K562 target cells. Following standard pre-processing, quality control checks, and dimensionality reduction, transcriptionally distinct cellular clusters were identified and annotated based on canonical NK cell marker genes. Focussing on the traditionally cytotoxic CD56dimCD16+ NK cell population, we observed that four of the seven sub-clusters were enriched at resting state, two sub-clusters were enriched following activation, and one sub-cluster remained unchanged between resting and activated states. Having identified the clusters of NK cells that respond to the K562 challenge, we next utilised the CellOracle analytical pipeline to identify the top predicted TFs driving NK cells towards this activated state. First, cell-state-specific gene regulatory network models were inferred for each cluster to identify and select predicted “active” TFs. Next, in silico gene perturbation and pseudotime calculations were performed to investigate the effects of knocking-out or knocking-in these TFs. A shortlist of several known and novel TFs were identified based on their likelihood of blocking or promoting NK cell activation. Finally, we validated the role of our top novel predicted TF on NK cell activity in vitro through mRNA overexpression and functional testing. Ultimately, our findings provide new insight into the molecular mechanisms underlying NK cell function. We anticipate this knowledge has significant implications for developing enhanced cellular therapies, providing new avenues through which the full potential of NK cells can be exploited to improve patient outcomes.