This study highlights the value of AI-powered, high-resolution modeling for TB surveillance in resource-limited settings. The approach supports the identification of underserved areas, improved resource targeting, and more strategic public health planning. Future integration of multidrug-resistant TB and active case-finding data may further enhance its impact and scalability.