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However, sex-specific progression patterns remain underexplored due to unclear clinical and molecular mechanisms. To address this gap, we developed a temporal autoencoder framework to identify sex-specific AD sub-phenotypes using longitudinal electronic health record (EHR) data from the OneFlorida+ Clinical Research Consortium. Sequential EHRs were encoded into latent representations and clustered to derive disease states, which were assembled into progression pathways. This approach uncovered five primary sex-stratified sub-phenotypes with distinct trajectories and phenotypic characteristics. Survival and cumulative prevalence analyses further revealed heterogeneous temporal dynamics of AD onset and comorbidity accumulation between female- and male-dominant groups. 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