<p dir="ltr">This thesis focusses on developing novel data driven oral drug formulation analysis methods by employing technologies such as Fourier transform analysis and generative adversarial learning. Data driven measurements have been addressing challenges in advanced manufacturing and analysis for pharmaceutical development for the last two decade. Data science combined with analytical chemistry holds the future to solving key problems in the next wave of industrial research and development. Data acquisition is expensive in the realm of pharmaceutical development, and how to leverage the capability of data science to extract information in data deprived circumstances is a key aspect for improving such data driven measurements. Among multiple measurement techniques, chemical imaging is an informative tool for analyzing oral drug formulations. However, chemical imaging can often fall into data deprived situations, where data could be limited from the time-consuming sample preparation or related chemical synthesis. An integrated imaging approach, which folds data science techniques into chemical measurements, could lead to a future of informative and cost-effective data driven measurements. In this thesis, the development of data driven chemical imaging techniques for the analysis of oral drug formulations via Fourier transformation and generative adversarial learning are elaborated. Chapter 1 begins with a brief introduction of current techniques commonly implemented within the pharmaceutical industry, their limitations, and how the limitations are being addressed. Chapter 2 discusses how Fourier transform fluorescence recovery after photobleaching (FT-FRAP) technique can be used for monitoring the phase separated drug-polymer aggregation. Chapter 3 follows the innovation presented in Chapter 1 and illustrates how analysis can be improved by incorporating diffractive optical elements in the patterned illumination. While previous chapters discuss dynamic analysis aspects of drug product formulation, Chapter 4 elaborates on the innovation in composition analysis of oral drug products via use of novel generative adversarial learning methods for linear analyses.</p>