Tuberculosis (TB) is a huge problem worldwide with about 50 % of the cases in BRICS countries. With an estimated 2.3 million reported cases in 2011 and an additional 1 million undocumented cases, India has the highest prevalence of TB in the world. Each year 2.8 million cases develop TB and about 1 million cases go unreported or undiagnosed. Solving the TB crisis has been a top public health priority for years. Machine learning (ML) techniques have been used world-wide for improving public health through development of better prognostic, diagnostic and predictive models. There is a need to detect the missed cases by early adoption of Computer Assisted Diagnosis (CAD) systems based on artificial intelligence (AI) technologies for TB detection. This will synergize with the global endeavours to close the gap in TB control.