Storm surge flooding caused by tropical cyclones is a devastating threat to coastal regions, and this threat is growing due to sea-level rise (SLR). Therefore, accurate and rapid projection of the storm surge hazard is critical for coastal communities. This study focuses on developing a new framework that can rapidly predict storm surges under SLR scenarios for any random synthetic storms of interest and assign a probability to its likelihood. The framework leverages the Joint Probability Method with Response Surfaces (JPM-RS) for probabilistic hazard characterization, a storm surge machine learning model, and a SLR model. The JPM probabilities are based on historical tropical cyclone track observations. The storm surge machine learning model was trained based on high-fidelity storm surge simulations provided by the U.S. Army Corps of Engineers (USACE). The SLR was considered by adding the product of the normalized nonlinearity, arising from surge-SLR interaction, and the sea-level change from 1992 to the target year, where nonlinearities are based on high-fidelity storm surge simulations and subsequent analysis by USACE. In this study, this framework was applied to the Chesapeake Bay region of the U.S. and used to estimate the SLR-adjusted probabilistic tropical cyclone flood hazard in two areas: one is an urban Virginia site, and the other is a rural Maryland site. This new framework has the potential to aid in reducing future coastal storm risks in coastal communities by providing robust and rapid hazard assessment that accounts for future sea-level rise. / Master of Science / Storm surge flooding, which is the rise in sea level caused by tropical cyclones and other storms, is a devastating threat to coastal regions, and its impact is increasing due to sea-level rise (SLR). This poses a considerable risk to communities living near the coast. Therefore, it is crucial to accurately and quickly predict the potential for storm surge flooding. This study aimed to develop a new way that can rapidly estimate peak storm surges under different sea-level rise scenarios for any random synthetic storms of interest and assess the likelihood of their occurrence. The approach is based on historical tropical cyclone datasets and a machine learning model trained on high-quality simulations provided by the US Army Corps of Engineers (USACE). The study focused on the Chesapeake Bay area of the US and estimated the probabilistic tropical cyclone flood hazard in two locations, an urban site in Virginia and a rural site in Maryland. This new approach has the potential to assist in reducing coastal storm risks in vulnerable communities by providing a quick and reliable assessment of the hazard that takes into account the effects of future sea-level rise.