The D-region ionosphere (60−90 km) has long been a region of interest to both researchers and the long range communication community due to its importance in over the horizon communication and radar systems. Unfortunately, there is a lack of in-situ measurements due to the difficulties of placing sensors in the necessary altitude range. Instead, through the use of very low frequency (VLF, 3-30 kHz) remote sensing techniques, researchers have been able to estimate the electrical properties of the D region along a small set of source-to-receiver paths. Typically, researchers will rely on either VLF transmitters or radio emissions from lightning known as radio atmospherics (sferics) as sources for their remote sensing techniques. These efforts have so far proved to be a valuable resources; however, they are limited to producing path-averaged electron density measurements on a small number of days at a time. Additionally, researchers have historically focused on either sferic-based approaches or VLF transmitter-based approaches, but rarely both. The work presented in this dissertation addresses multiple improvements to these methods. First a machine learning-based approach is used to determine VLF-transmitter-to-receiver path electron density profiles across over 400 days and over 150 nights. Then tomography is applied to sferic-based path average electron density profiles to produce 3D maps of electron density over the Gulf of Mexico and Southeastern United States. Finally, a tomography model is used to unify the output of the VLF-transmitter model and the sferic-based model. This represents the first time both VLF transmitters and sferics have been used together to produce 3D electron density maps of the D-region ionosphere.