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Background: Women face underdiagnosed cardiovascular disease (CVD)/stroke risks due to sex-specific pathophysiological mechanisms, including hormonal variations such as oestrogen decline, adverse pregnancy outcomes (APOs), endothelial dysfunction, autoimmune-mediated factors, and sexual dimorphism in cardiac remodelling. Conventional risk assessment tools, predominantly calibrated to male pathophysiology, lack sensitivity in detecting these female-specific determinants. We hypothesise that artificial intelligence (AI), machine learning (ML) and deep learning (DL) may offer a transformative approach by integrating multimodal data, including pathological biomarkers, clinical history, and vascular imaging, to enable precision CVD/stroke risk stratification, pending rigorous external validation in sex-stratified cohorts. Method: This narrative review adopts a PRISMA-informed study selection framework and oversees gender-specific biomarkers, including vasoactive peptides (adrenomedullin), adipocytokines (adiponectin), inflammatory mediators (hs-CRP, IL-6), and thrombogenic factors (homocysteine, D-dimer), alongside clinical variables (APOs, autoimmune disorders) and ultrasonographic markers, carotid intima-media thickness (cIMT), plaque burden and plaque area (PA). Advanced ML/DL algorithms were employed to synthesise these heterogeneous datasets, identifying nonlinear interactions for better outcomes. Findings: Key insights reveal that hormonal dynamics (e.g., hypoestrogenism post-menopause) modulate CVD risk, while APOs induce persistent endothelial dysfunction and subclinical atherosclerosis. Biomarker sexual dimorphism is evident; hs-CRP exhibits higher baseline levels in women, whereas adiponectin declines with metabolic dysfunction. Radiomic features (cIMT progression, plaque morphology) are a well-established biomarker for CVD risk stratification. Conclusions: The integration of AI-driven multimodal systems holds the potential to enable a paradigm shift from population-based to personalised risk assessment, addressing critical gaps in female CVD health. However, this potential is currently at the early validation stage, and widespread clinical implementation requires prospective, externally validated, and ethnically diverse studies. Future applications should incorporate longitudinal biomarker profiling and advanced imaging, namely shear wave elastography and plaque radiomics, to optimise predictive models.