Sporadic-E manifests as regions of enhanced ionization, primarily occurring 90-130 km above Earth's surface. These irregularly ionized layers can reflect or degrade radio waves propagating through the ionosphere and impact applications such as satellite and HF communications, GPS navigation and positioning, and over-the-horizon radar. In order to effectively operate in these complex electromagnetic environments, a global understanding and accurate characterization of sporadic-E is critical. In this research, advanced signal processing and machine learning techniques were used to develop models that are able to characterize the ionospheric phenomena in terms of occurrence, intensity, and location. Models were developed for the cases where in-situ radio occultation measurements are available (spatio-temporally sparse), and when they are not. Additionally, a global sporadic-E climatology study was carried out using the developed models which agrees with known physics and observations, giving higher confidence in the models.