This paper presents an analysis of unit-level data from India’s first national Time Use Survey (TUS), conducted in 2019.  Both men and women are engaged in unpaid as well as paid activities, but there are important gender differences in the time they spend on various activities, which vary by sector, education, age groups, income (proxied by monthly per capita quartiles) and social group. Following an analysis of gender differences in participation and time spent on various types of work, the paper narrows down to identifying determinants of time spent by women on specific domestic work and care work activities, using both standard econometric techniques as well as Machine Learning (ML) techniques, specifically Random Forest decision tree method, combined with SHAP estimation to gauge feature importance and directionality. The result from the two techniques overlap partially. ML results reveal that higher number of children and dependent adults, presence of adults with special needs, leads to higher-than-average time spent on care work. Access to piped gas, electricity decreases time spent on cooking preparation and increases time spent on paid work and leisure time. Presence of other women in the household or the presence of paid helpers does not affect women's time spent on either domestic or care work. Presence of unemployed men in the household reduces women's time spent on care work but does not affect time spent on domestic work. The paper presents back-of-the-envelope calculations of state provision of childcare through anganwadi centres (AWCs) on the ability of women to participate in paid work. Estimates reveal that this would lead to a significant increase in both incomes and employment for women, but would lead to the creation of employment opportunities that could benefit men as well as women. This could potentially lead to a significant increase in India’s GDP.  These results highlight the 3Rs framework: recognize, reduce, redistribute: women’s overwhelming involvement in unpaid domestic and care work. The paper highlights the need for investment in the care economy infrastructure, which will boost employment via the creation of labour intensive, green jobs.