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Data scientists and development experts seeking to quantify social indicators of development in Southeast Asia have routinely encountered difficulties in identifying reliable priori, collecting novel information, and promoting societal equity as a necessary tenet of development. Historically and through the 21st century, strict patriarchal norms have dominated the social routines of women in regions like India, Pakistan, Nepal, Sri Lanka, Indonesia, and Bhutan, leading to significant disparities in Southeast Asian women’s observable wellbeing (e.g., their economic, physical, and mental health, as documented in surveys and government records). Nepal, for example, boasts an estimated imbalance of 28% between male and female incomes. Beyond the more visible components of gender inequality, however, there lurks a deeper problem, one that a recent UN Global Pulse Report from 2018 describes as an unignorable trend of invisibility concerning female-focused data. The lacking availability of gender-disaggregated, granular data that illustrates the more minute aspects of gender equality, especially in areas like Southeast Asia where women’s access to information technology and self-reporting methods of data pooling is limited, poses a distinctly unique problem for data scientists hoping to analyze gendered facets of development on both local and global scales. This paper aims to introduce several methods of data analysis and event prediction as a collective partial solution to calculating the extent of Southeast Asia’s progress towards achieving gender equality on multiple developmental fronts. Specifically, this methodological identification discusses in-depth the potential benefits of using applied Bayesian generalized linear modelling executed by predictive machine learning algorithms and localized, qualitatively-layered geographic information systems for data mapping. By making genuine efforts to include a diverse group of researchers – especially in regards to gender identity – in future projects that may choose to utilize this approach and including as much geo-specific data as possible in creating a rich context for the illustration of gender-disaggregated trends and phenomena, contemporary feminist-minded and data-focused literature alike suggests the results from such studies will prove to be both usable and incredibly informative.