The Mpox virus, a zoonotic orthopoxvirus, poses significant public health risks due to its capacity to cause outbreaks with high morbidity. Recent advancements in genomics and bioinformatics have enabled in-depth analysis of viral evolution, transmission, and pathogenicity through DNA and RNA sequencing. Integrating artificial intelligence (AI) and machine learning (ML) techniques, particularly deep learning, with genomic data offers a powerful approach to predicting viral behaviour and mutations. This study utilizes MATLAB to harness these advanced computational techniques, aiming to improve the predictive modelling of the Mpox virus. The research involves collecting and analysing Mpox DNA and RNA sequences using MATLAB's robust AI, ML, and deep learning toolboxes. By developing predictive models, this study seeks to uncover patterns that could inform predictions about viral mutation rates and evolutionary trends. MATLAB's environment allows for efficient data preprocessing, model training, and validation, ensuring accurate and interpretable results. This approach not only enhances our understanding of the Mpox virus but also provides a framework for applying AI-driven genomics in managing and preventing future viral outbreaks. The findings from this research could be instrumental in informing public health strategies and vaccine development, potentially reducing the impact of future Mpox outbreaks through early prediction and intervention.