This study introduces a comprehensive and cutting-edge predictive model for tuberculosis (TB) incidence, leveraging the power of Artificial Intelligence (AI) and Machine Learning (ML) techniques, with a focus on Convolutional Neural Networks (CNN). Implemented through MATLAB, this model aims to significantly improve the accuracy of TB predictions by incorporating diverse and multi-dimensional data sources and applying state-of-the-art algorithms. The model development involves a thorough process of data integration, including demographic, environmental, and clinical datasets, to ensure a holistic approach to prediction. The CNN architecture is meticulously designed and optimized within the MATLAB environment, utilizing advanced layers and activation functions to enhance model performance. Training protocols include extensive data augmentation and hyperparameter tuning to refine the predictive capabilities. Validation is performed using rigorous cross-validation methods and a variety of performance metrics such as accuracy, sensitivity, specificity, and ROC curves, ensuring the model's robustness and reliability. The study also conducts a comparative analysis of the CNN-based model against traditional statistical models and other ML algorithms, highlighting the superiority and potential biases of each. The effectiveness of the model is demonstrated through real-world case studies, providing valuable insights for public health policy and TB control strategies. This transformative approach aims to revolutionize TB prediction and significantly impact global health outcomes.