Preeclampsia (PE) is a life-threatening obstetric disorder that can lead to significant gestational and fetal morbidity and mortality. Recent studies indicate that individuals with a history of PE are at an elevated risk of developing cardiovascular diseases (CVDs) later in life. Despite the clinical association between PE and CVDs, the shared molecular mechanisms and regulatory genes underlying these conditions remain insufficiently characterized. Through this study, we focused on elucidate the concordant molecular pathways and endowment therapeutic targets implicated in both PE and CVDs to enhance early detection and intervention strategies. An integrative systems biology framework, combined with machine learning techniques, was applied to determine key Differentially expressed genes (DEGs) from transcriptomic data. Functional enrichment was conducted using Gene Ontology (GO) and pathway analysis to uncover biological processes and signaling cascades. Hub genes were identified by building protein–protein interaction (PPI) networks. Additionally, transcription factor (TF)-gene and microRNA (miRNA)-gene regulatory networks were explored to highlight upstream regulatory elements. Finally, computational drug analysis revealed potential therapeutic compounds targeting both conditions, which may function as dual-purpose agents for treatment and serve as prognostic biomarkers for cardiovascular risk assessment in women previously diagnosed with preeclampsia.