Severe weather events like hurricanes and tornadoes pose major risks globally, underscoring the critical need for accurate forecasts to mitigate impacts. While advanced computational capabilities and climate models have improved predictions, lack of high-resolution initial conditions still limits forecast accuracy. The Atlantic's "Hurricane Alley" region sees most storms arise, thus needing robust in-situ ocean data plus atmospheric profiles to enable precise hurricane tracking and intensity forecasts. Examining satellite datasets reveals radio occultation (RO) provides the most accurate 5-25 km altitude atmospheric measurements. However, below 5 km accuracy remains insufficient over oceans versus land areas. Some recent benchmark study e.g. Patil Iiyama (2022), and Wei Guan (2022) in their work proposed the use of deep learning models for sea surface temperature (SST) prediction in the Tohoku region with very low errors ranging from 0.35°C to 0.75°C and the root-mean-square error increases from 0.27°C to 0.53°C over the over the China seas respectively. The approach we have developed remains unparalleled in its domain as of this date. This research is divided into two parts and aims to develop a data driven satellite-informed machine learning system to combine high-quality but sparse in-situ ocean data with more readily available low-quality satellite data. In the first part of the work, a novel data-driven satellite-informed machine learning algorithm was implemented that combines High-Quality/Low-Coverage in-situ point ocean data (e.g. ARGO Floats) and Low-Quality/High-Coverage Satellite ocean Data (e.g. HYCOM, MODIS-Aqua, G-COM) and generated high resolution data with a RMSE of 0.58◦C over the Atlantic Ocean.The second part of the work a novel GNN algorithm was implemented on the Gulf of Mexico and showed it can successfully capture the complex interactions between the ocean and mimic the path of a ARGO floats with a RMSE of 1.40◦C. / Doctor of Philosophy / Severe storms like hurricanes and tornadoes are a major threat around the world. Accurate weather forecasts can help reduce their impacts. While climate models have improved predictions, lacking detailed initial conditions still limits forecast accuracy. The Atlantic's "Hurricane Alley" sees many storms form, needing good ocean and atmospheric data for precise hurricane tracking and strength forecasts. Studying satellite data shows radio occultation provides the most accurate 5-25 km high altitude measurements over oceans. But below 5 km accuracy remains insufficient versus over land. Recent research proposed using deep learning models for sea surface temperature prediction with low errors. Our approach remains unmatched in this area currently. This research has two parts. First, we developed a satellite-informed machine learning system combining limited high-quality ocean data with more available low-quality satellite data. This generated high resolution Atlantic Ocean data with an error of 0.58°C. Second, we implemented a new algorithm on the Gulf of Mexico, successfully modeling complex ocean interactions and hurricane paths with an error of 1.40°C. Overall, this research advances hurricane forecasting by combining different data sources through innovative machine learning techniques. More accurate predictions can help better prepare communities in hurricane-prone regions.