Dengue outbreaks have become a global concern, affecting many regions such as the Americas, Africa, the Middle East, Asia, and the Pacific Islands. Over the past two decades, there has been a notable rise in dengue cases worldwide, with significant impacts observed in countries like Brazil and Bangladesh. Moreover, in the United States, local dengue transmission has been reported in a few states, including Florida, Hawaii, Texas, Arizona, and California. Numerous studies have demonstrated the correlation between climate factors—such as temperature and rainfall—and dengue, Zika, chikungunya, and yellow fever transmission. Specifically, elevated temperatures have been linked to an increased dengue infection risk, while extreme rainfall events have been shown to decrease this risk. In this project, we deploy deep learning time series analysis to analyze climate and epidemiological data in order to forecast dengue dynamics.