Poor ambient air quality poses a substantial global health threat. However, accurate measurement remains challenging, particularly in countries such as India where ground monitors are scarce despite high expected exposure and health burdens. This lack of precise measurements impedes understanding of changes in pollution exposure over time and across populations. Here, we develop open-source daily fine particulate matter (PM2.5) datasets at a 10-kilometer resolution for India from 2005 to 2023 using a two-stage machine learning model validated on held-out monitor data. Analyzing long-term air quality trends, we find that PM2.5 concentrations increased across most of the country until around 2016 and then declined partly due to favorable meteorology in southern India. Recent reductions in PM2.5 were substantially larger in wealthier areas, highlighting the urgency of air quality control policies addressing all socioeconomic communities. To advance equitable air quality monitoring, we propose additional monitor locations in India and examine the adaptability of our method to other countries with scarce monitoring data.