Ferroptotic cell death is a regulated process that is governed by iron-dependent membrane lipid peroxide accumulation that plays a pathogenic role in several disease-related settings. The use of ferroptosis-related genes (FRGs) to distinguish active tuberculosis (ATB) from latent tuberculosis infection (LTBI) among children, however, remains to be analysed. Tuberculosis-related gene expression data and FRG lists were obtained, respectively, from Gene Expression Omnibus (GEO) and FerrDb. Differentially expressed FRGs (DE-FRGs) detected when comparing samples from paediatric ATB and LTBI patients were explored using appropriate bioinformatics techniques, after which enrichment analyses were performed for these genes and hub genes were identified, with these genes then being used to explore potential drug interactions and construct competing endogenous RNA (ceRNA) networks. The GSE39939 dataset yielded 124 DE-FRGs that were primarily related to responses to oxidative, chemical and extracellular stimulus-associated stress. In total, the LASSO and SVM-RFE algorithms enabled the identification of nine hub genes (MAPK14,EGLN2,IDO1,USP11,SCD,CBS,PARP8,PARP16,CDC25A) that exhibited good diagnostic utility. Functional enrichment analyses of these genes suggested that they may govern ATB transition from LTBI through the control of many pathways, including the immune response, DNA repair, transcription, RNA degradation, and glycan and energy metabolism pathways. The CIBERSORT algorithm suggested that these genes were positively correlated with inflammatory and myeloid cell activity while being negatively correlated with the activity of lymphocytes. A total of 50 candidate drugs targeting 6 hub DE-FRGs were also identified, and a ceRNA network was used to explore the complex interplay among these hub genes. The nine hub FRGs defined in this study may serve as valuable biomarkers differentiating between ATB and LTBI in young patients.