Dust storms pose significant challenges globally, impacting various aspects of human life, the environment, and infrastructure. This paper focuses on the predictive analysis of dust storms using GIS, artificial intelligence, and spatiotemporal data. Apart from their environmental consequences, dust storms also have profound health implications, including respiratory diseases and lung cancer. This paper aims to explore the MaxEnt modeling technique for dust storm forecasting, considering the complex interplay of natural and anthropogenic factors. By leveraging GIS, artificial intelligence, and spatiotemporal data, this research seeks to enhance our understanding of dust storm dynamics and improve prediction accuracy, thereby facilitating proactive mitigation strategies and risk management efforts.