Ionospheric scintillation are signal perturbations caused by the interaction between the Earth’s geomagnetic field and the Sun’s activity and are apparent through rapid modifications in radio waves. Such perturbations are the most prevalent source of uncertainties in the position solution for Global Navigation Satellite Systems (GNSS). Since GNSS provide essential services for multiple industries and even everyday life, understanding ionospheric scintillation is essential. Geomagnetic storms are known to create disturbances in the ionosphere by increasing the total electron content (TEC). Therefore, this project highlights the relationship between geomagnetic storms and ionospheric scintillation through the analysis of processed GNSS data and proposes techniques for the identification and classification of scintillation in the mid-latitude region. Utilizing phase and amplitude data collected from two GPS Receivers installed in Daytona Beach, FL, possible events that correlate with scintillation observed during the storm were studied. A GI minor geomagnetic storm, measured to be -10 nanoTesla, that took place on January 31st, 2019 was studied because a significant spike in both phase and amplitude was observed. The implementation of machine learning is also explored through the development of an unsupervised k-means clustering algorithm that will identify the distribution of data points and classify scintillation.