Abstract: Parkinson’s disease (PD) is a prevalent age-related neurodegenerative disorder worldwide. Men are ~2 times more likely to develop PD compared to women. PD is starkly different between men and women; from age of onset, symptoms, and severity. While there have been some studies identifying women-specific health factors (hormonal and immunological changes due to experiences like menses, pregnancies, and menopause), these factors are often ignored in predictive models and treatment plans. Studies have reported that women experience higher rates of delayed or incorrect PD diagnosis, are less likely to receive therapy or see a specialist once diagnosed, and have a lower health-related quality of life than men. Despite the stark differences seen in the risk and severity of PD between men and women, there is no universally accepted reason(s) behind it, leaving women, especially Latinas, poorly understood in PD. To combat this great overlook for Latinas and women’s health, we will deploy our women’s questionnaire to 1,208 Latinas being recruited all across the Americas and the Caribbean through LARGE-PD, PD GENEration, and The Cleveland Clinic. We will use novel machine learning approaches to generate a first-of-its-kind predictive model for PD severity (determined by the PD clinical score and symptoms profile) that includes the women-specific health factors alongside clinical, demographic, and genetic factors. Our goal is to create PD predictive models that are specific to women and moreover, Latinas, shifting the paradigm to emphasize the importance of sex, culture, and ethnicity in PD. In addition, this project will influence PD research to incorporate sex-specific experiences alongside genetics and other factors. By identifying novel associations between women-specific health factors, genetics, and PD severity, this study has the potential to reduce inequities in healthcare for Latinas – not just for PD but also perhaps for other neurological conditions.