Surveillance is critical for interrupting transmission of global epidemics. Research has highlighted gaps in the surveillance for tuberculosis that range from failure to collect real-time data to lack of standardization of data for informed decision-making at different levels of the health system. Our research aims to advance conceptual and methodological foundations for the development of a learning surveillance system for Tuberculosis, that involves systematic collection, analysis, interpretation, and feedback of outcome-specific data. It would concurrently involve the health care delivery system, public health laboratory, and epidemiologists. For our study, we systemically framed the cyber environment of TB surveillance as an ontology of the learning surveillance system. We validated the ontology by binary coding of dimensions and elements of the ontology with the metadata from an existing surveillance platform-GPMS TB Transportal. Results show GPMS TB Transportal collects a critical range of data for active case investigation and presumptive case screening for identifying and detecting confirmed TB cases. It is therefore targeted at assisting the Active Case Finding program. Building on the results, we demonstrate enhanced surveillance strategies for GPMS that are enumerated as pathways in the ontology. Our analysis reveals the scope for embedding learning surveillance pathways for digital applications in Direct Benefit Transfer, and Drug Resistance Treatment in National TB Elimination Programme in India. We discuss the possibilities of developing the transportal into a multi-level computer-aided decision support system for TB, using the innumerable pathways encapsulated in the ontology.