<p>This study offers a comprehensive analysis of infectious disease transmission, focusing specifically on HIV transmission among populations classified by biological sex. The primary objective of the project is to develop a sex-specific compartmental model that incorporates memory-dependent dynamics and time lags to more accurately depict the temporal evolution of infections. The work utilised bifurcation analysis to systematically assess the stability properties of the disease-free and endemic equilibrium points. A sensitivity study clarifies the epidemiological factors that significantly impact transmission intensity in the community. The analytical conclusions are validated through computational experiments, with outcomes illustrated in clear graphical representations. The results emphasise that timely identification of infection, along with tailored antiretroviral medication, significantly improves the likelihood of epidemic control. The authors advocate for further research that integrates more intricate transmission channels and ground model outputs with real epidemiology data crucial measures for enhancing intervention frameworks in light of evolving disease transmission dynamics.</p>