Communicable diseases pose significant threats at local, regional, and global levels, often leading to epidemics or pandemics. An epidemic refers to a sudden increase in the number of cases of an infectious disease above what is normally expected in a given population. Examples include cholera, measles, malaria, and dengue fever. Pandemics, however, can result in widespread illness, significant loss of life, and severe social and economic consequences. Concerns about potential pandemic diseases, such as new strains of influenza and severe acute respiratory syndrome (SARS) remain critical. This study presents a deep learning framework based on Geographic Information Systems (GIS) to predict the spatial distribution of epidemic infectious diseases. The framework combines the strengths of deep learning and GIS techniques, both of which offer exceptional capabilities in the field of epidemiology. The study outlines the key steps involved in developing the proposed framework and explains its operational functionality. The proposed framework aims to enhance decision-making efficiency, assist governmental authorities in generating sustainable strategies, and establish appropriate protocols to control epidemics, particularly in high-risk areas. By predicting vulnerable areas, the framework helps mitigate the risks associated with outbreaks and protects social and economic stability. Choosing an appropriate framework requires consideration of several key factors, including those relevant to the spread of diseases or epidemics, accuracy, flexibility, and validation.