Mycobacterium tuberculosis (Mtb) is a globally important infectious agent that continues to kill 1.5 million people a year and is transmitted when individuals with pulmonary tuberculosis (TB) disease cough. Four million cases of the estimated 10.6 million new tuberculosis (TB) disease cases annually remain undiagnosed and there are no non-sputum-based diagnostic tests specifically for pulmonary tuberculosis. Non-sputum-based biomarkers that can identify people with transmissible pulmonary TB are urgently needed. I investigated the hypothesis that proteins released into the plasma during active pulmonary TB will be clinically useful biomarkers, which will exhibit variance according to host phenotypic characteristics. I undertook non-depletion tandem mass spectrometry to investigate plasma protein expression in active pulmonary tuberculosis patients compared to uninfected patients from a male South African and Peruvian cohort. I performed bioinformatic analysis using linear modelling and network correlation analysis to identify differentially expressed proteins. Additionally, I investigated the biology of the plasma proteome of pulmonary tuberculosis using gene ontology and protein variance analysis tools. I subsequently validated candidate protein biomarkers using complementary antibody-based protein detection methods in a separate mixed sex patient cohort from diverse ethnic backgrounds. I explored the ability of validated biomarkers to discriminate individuals with TB from healthy controls and individuals with symptoms of pulmonary TB with an alternative diagnosis, termed respiratory symptomatics. Following combinatorial analysis, I identified a six-protein panel that could distinguish TB from both healthy individuals and respiratory symptomatics with extremely high sensitivity, specificity, and accuracy, indicating that this panel could be used to facilitate the diagnosis of TB and help close the case-detection gap that is fuelling the pandemic.