The development of novel therapeutic approaches has taken precedence in the fight against tuberculosis control due to the global upsurge in multi-drug-resistant tuberculosis (MDR-TB). Since MDR-TB strains are resistant to first-line therapies, finding new drugs is a major challenge. Computational modeling has emerged as a powerful tool in this fight, providing valuable insights into resistance mechanisms and accelerating the development of new anti-TB drugs. This chapter aims to give an overview of the various computational strategies reported in drug discovery for MDR-TB with a comprehensive overview of several structure-based and ligand-based computational methods. Additionally, notable applications of these methods in the discovery of novel anti-TB compounds have also been mentioned. This chapter also delves into the emergence of AI, and ML applications which have proved to be useful both in the design of new compounds and in the development of theoretical models that can predict resistance to the MDR-TB therapy.