Tuberculosis (TB) kills 1.3 million people annually and remains the world's deadliest bacterial disease. The standard four-drug RIPE regimen achieves only 85% cure rates and causes drug-induced hepatotoxicity in 25-37% of patients. Hydroxychloroquine (HCQ), an FDA-approved antimalarial, has been shown to synergise with pyrazinamide (PZA) by inhibiting the BCRP-1 efflux pump and raising phagolysosomal pH, increasing intracellular PZA concentrations (FICI 0.38 in vitro). The AIPH-TB computational framework (Artificial Intelligence Physicochemical Harmonisation for Tuberculosis) uses multi-objective reinforcement learning, Gaussian process regression, and a digital twin macrophage simulator to identify an AI-optimised dosing schedule that maximises this synergy (PZA 1,500 mg + HCQ 200 mg at 0800 and HCQ 200 mg at 2000), maintaining phagolysosomal pH within 5.2-5.8 for 18 of 24 hours. The computational model predicts FICI 0.28 (strongly synergistic), 9.4-fold increase in intracellular PZA concentration, 99.5% cure rate, and \<1.5% hepatotoxicity. This Phase II randomised controlled trial will test whether the AI-optimised PYZ-HCQ protocol is superior to standard RIPE in 200 newly-diagnosed drug-sensitive pulmonary TB patients over 6 months of treatment with 6 months of follow-up.