The development of cost-optimal yet sustainable manufacturing processes is a growing priority in the pharmaceutical industry, with many institutions hoping to become carbon neutral in the near future. However, the majority of research and development (R&D) activities within the sector still rely heavily on the use of experimental trials, making the design of new processes labour intensive and materially expensive. Consequently, process development costs have been rising amid increasingly stringent environmental regulations which demand the deployment of greener manufacturing platforms. It has therefore been postulated that mathematical modelling should be used as a means of accelerating R&D activities by using predictive science to reduce the number of experimental trials required. However, there is currently a lack of organised frameworks available to support this type of work – especially when it comes to process optimisation. Considering this, this PhD presents numerical modelling frameworks which can be used to quickly simulate, visualise and optimise pharmaceutical manufacturing processes. Further to this, it demonstrates how these processes may be modelled using data which is already routinely collected by pharmaceutical companies during early-stage drug development (i.e., kinetic reaction and solubility data). To achieve this goal, high-fidelity process models have been developed for common unit operations seen in pharmaceutical manufacturing (i.e., reactors, crystallisers and liquid-liquid extraction units). With the identification, development and parameterisation of fit-for-purpose kinetic reaction and solubility models forming an important part of this work. Meanwhile, established process economics models have been used alongside green metrics (e.g., E-factor, Scope 1 and 2 emissions) to assess the cost, emissions and material efficiency of these processes. To display the capabilities of these tools, manufacturing processes involved in the production an experimental anti-cancer drug, adavosertib (AZD1775), and a non-steroidal anti-inflammatory drug (NSAID), flurbiprofen, have been studied – though the principles used to conduct this work could be easily applied to a wide range of other pharmaceuticals. Consequently, the design space associated with each process has been visualised to explore the impact of solvent choice, system, temperature, process volume, and reagent concentrations on process viability – ultimately, allowing their manufacturing costs, campaign times and carbon emissions to be minimised.