In Jharkhand, India, an area marked by socioeconomic difficulties and restricted access to digital education, this research examines the ongoing gender differences in computer literacy among rural communities. This study uses machine learning methods, namely Random Forest Regression, to predict how gender discrimination in computer literacy would develop until 2030. In order to provide a data-driven basis for focused policy actions, the study focusses on identifying important socioeconomic, educational, and cultural elements that influence to the digital partition. According to the report, women in rural Jharkhand have several obstacles that prevent them from learning digital skills, such as a lack of educational options, ingrained cultural norms, and financial limitations. Although the evaluation of the model, which obtained an R2 score of 1.0, indicates the potential for overfitting because of the perfect fit, it also emphasizes the predictive accuracy of applying machine learning for such socio-educational projections. According to projections, the gender gap in computer literacy would continue to exist at about 20% by 2030 unless significant policy reforms are made. The results highlight how urgent it is to put in place socioeconomic initiatives and inclusive digital education policies to empower women in rural regions. Besides also bringing to the current literature on gender inequality and digital literacy this research provides stakeholders virtual consultation on how the digital divide may be closed to support increased a more inclusive digital economy in India.