To put in place inclusive strategies that increase the resilience of women and men in all their diversity, there is an urgent need to better understand the gendered effects of climate change across countries. To achieve this, this paper explores the connections between phenomena related to climate change and gender related outcomes in Bangladesh, Cambodia, Nepal, the Philippines and Timor-Leste. In particular, it tests these associations by utilizing random forest machine learning techniques and binary logistic regression analysis, on a data set that integrates data from Demographic and Health Surveys (DHS) and geographical information systems (GIS).