This study analyzes the geographic accessibility of depression care centers in Mexico using Geographic Information Systems (GIS), aiming to provide robust evidence for improving equitable access to in-person mental health services. Leveraging open data from the Mexican Secretaría de Salud (Secretariat of Health SEDESA) from 2023 to 2025, the research identifies active facilities treating depression and calculates the minimum distance from the geographic centroids of more than 50,000 localities across the country. The methodology integrates spatial analysis tools in QGIS and Python to compute distance matrices and determine the average proximity of each locality to health centers, categorized by level of care (primary, secondary, tertiary). The results were mapped to visualize national coverage patterns and revealed a striking inversion of the expected pyramidal structure of the healthcare system where secondary level centers outnumber primary ones in treating depression. This suggests delayed entry into care and highlights the need to strengthen early intervention at the primary level. This research presents a replicable and scalable spatial analysis model that uses open geostatistical and health datasets to quantify access to mental health services. It introduces a high-resolution national accessibility baseline that can be expanded in future work to include travel time, road networks, and socioeconomic correlates. The resulting geospatial datasets are made publicly available to support further studies and policy development. By combining spatial analytics and public health data, the study provides a novel lens to evaluate structural inequalities in mental healthcare provision in Latin America. These findings offer critical insights for designing evidence-based policies and resource allocation strategies to ensure timely and equitable access to depression care in Mexico.