Parkinson's disease (PD) is a neurodegenerative disorder with significant sex differences in incidence and presentation (Haaxma et al., 2007; Martinez-Martin et al., 2012; Bloem et al., 2021), yet the underlying sex-specific transcriptomic changes in the substantia nigra (SNc) remain overlooked. Previous studies investigating gene regulation in PD have often been limited by sample size and experimental design. We conducted the largest meta-analysis to date to identify nigral transcriptomic changes affected by both PD and sex, focusing on disease-by-sex interaction effects. We gathered 15 publicly available human PD substantia nigra transcriptomic data. We then performed a meta-analysis across multiple datasets (4,254 genes) to identify genes with significant disease-by-sex interaction (PD-by-sex) effects, which were then classified into six distinct expression patterns. We employed Weighted Gene Co-expression Network Analysis (WGCNA) to identify trait-associated modules. Finally, transcription factor binding site (TFBS) analysis was performed to uncover putative regulatory mechanisms. We identified 562 genes with significant disease-by-sex interaction effects, hereafter termed "PD-by-sex genes". To correctly and accurately interpret these expressive patterns, we innovatively categorized them into six groups. The predominant pattern (Pattern 5) involved genes upregulated in PD males but downregulated in PD females (e.g., STEAP3), enriched for iron homeostasis pathways. Other patterns were enriched for mitochondrial processes. WGCNA identified distinct co-expression modules correlated with specific sex-and-disease states, including a hub gene (VBP1) associated with control males. Critically, TFBS analysis of PD-by-sex genes revealed enrichment for four key transcription factors: TP53, FLI1, THAP11, and ZNF143, linking TP53, for instance, to the regulation of mitochondrial-related interaction genes. This study provides a comprehensive map of sex-specific transcriptomic alterations in the PD SNc. We identify hundreds of novel interaction genes and identify key regulatory networks and transcription factors driving these differences, offering a rich set of novel targets for future sex-specific biomarker discovery and therapeutic development.