Advancing SDG5: Machine Learning and Statistical Graphics for Women’s Empowerment and Gender Equity

In pursuit of sustainable development goal 5 (SDG5), this study underscores gender equity and women’s empowerment as pivotal themes in sustainable development. It examines the drivers of women’s empowerment, including education, economics, finance, and legal rights, using data from n = 223 individuals, primarily women (68.4%) aged 20–30 (69.6%). The research methodology integrates descriptive statistical measures, machine learning (ML) algorithms, and graphical representations to systematically explore the fundamental research inquiries that align with SDG5, which focuses on achieving gender equity. The results indicate that higher educational levels, captured through ordinal encoding and correlation analyzes, are strongly linked to increased labor market participation and entrepreneurial activity. The random forest (RF) and support vector machine (SVM) classifiers achieved overall accuracies of 89% and 93% for the categorization of experience, respectively. Although 91% of women have bank accounts, only 47% reported financial independence due to gendered barriers. Logistic regression correctly identified financially independent women with a 93% recall, but the classification of non-independent participants was less robust, with a 44% recall. Access to legal services, modeled using a neural network, was a potent predictor of empowerment (F1-score 0.83 for full access cases), yet significant obstacles persist for those uncertain about or lacking legal access. These findings underscore that, while formal institutional access is relatively widespread among educated women literate in the digital world, perceived and practical barriers in the financial and legal realms continue to hinder empowerment. The results quantify these effects and highlight opportunities for tailored, data-driven policy interventions targeting persistent gaps.