Air pollution remains poorly characterized across sub-Saharan Africa due to sparse monitoring infrastructure. We developed a multi-model machine learning framework to generate a 21-year, high-resolution PM_2.5 dataset for Ghana, revealing pronounced spatial and seasonal variability, persistent geographic exposure disparities, and contrasting long-term trends. These findings provide new evidence to support air-quality management, health assessments, and environmental policy in data-sparse regions.