The use of databases for managing spatial data is widespread due to the efficiency of traditional SQL databases like Azure SQL. However, the exponential growth of data from sources like social media has led to the popularity of NoSQL databases such as MongoDB that handle large volumes of data effectively. NoSQL databases, including MongoDB, have built-in support for geospatial queries, making them suitable for managing geospatial data. Geospatial data combines geometric and geographic information and is represented by spatial datatypes like Point, LineString, and Polygon. MySQL and MongoDB both support geospatial data, but limited studies are comparing their performance in geospatial queries. An experiment was conducted to compare the fetch speed of geospatial data in these databases. The results were analyzed using graphs and related studies to draw conclusions, which showed that MongoDB performed slower fetch requests than MySQL. Future studies can use more data points and different queries.