Modelling ionospheric total electron content (TEC) is an important area of interest for radio wave propagation, geodesy, surveying, the understanding of space weather dynamics and error correction in relation to Global Navigation Satellite Systems (GNNS) applications. With the utilisation of improved ionosonde technology coupled with the use of GNSS, the response of technological systems due to changes in the ionosphere during both quiet and disturbed conditions can be historically inferred. TEC values are usually derived from GNSS measurements using mathematically intensive algorithms. However, the techniques used to estimate these TEC values depend heavily on the availability of near-real time GNSS data, and therefore, are sometimes unable to generate complete datasets. This thesis investigated possibilities for the modelling of TEC values derived from the South African Global Positioning System (GPS)receiver network using linear regression methods and artificial neural networks (NNs). GPS TEC values were derived using the Adjusted Spherical Harmonic Analysis (ASHA) algorithm. Considering TEC and the factors that influence its variability as “dependent and independent variables” respectively, the capabilities of linear regression methods and NNs for TEC modelling were first investigated using a small dataset from two GPS receiver stations. NN and regression models were separately developed and used to reproduce TEC fluctuations at different stations not included in the models’ development. For this purpose, TEC was modelled as a function of diurnal variation, seasonal variation, solar and magnetic activities. Comparative analysis showed that NN models provide predictions of GPS TEC that were an improvement on those predicted by the regression models developed. A separate study to empirically investigate the effects of solar wind on GPS TEC was carried out. Quantitative results indicated that solar wind does not have a significant influence on TEC variability. The final TEC simulation model developed makes use of the NN technique to find the relationship between historical TEC data variations and factors that are known to influence TEC variability (such as solar and magnetic activities, diurnal and seasonal variations and the geographical locations of the respective GPS stations) for the purposes of regional TEC modelling and mapping. The NN technique in conjunction with interpolation and extrapolation methods makes it possible to construct ionospheric TEC maps and to analyse the spatial and temporal TEC behaviour over Southern Africa. For independent validation, modelled TEC values were compared to ionosonde TEC and the International Reference Ionosphere (IRI) generated TEC values during both quiet and disturbed conditions. This thesis provides a comprehensive guide on the development of TEC models for predicting ionospheric variability over the South African region, and forms a significant contribution to ionospheric modelling efforts in Africa.