New data methods for forecasting systems of regional sea level prediction based on climate variables using both classical time series models and modern machine learning methods will be presented. Sea level rise induced by climate change poses a significant threat to coastal cities, many of which serve as major economic hubs due to their strategic coastal locations, such as New York and Shanghai. To mitigate the increasing risks, accurate predictive models are urgently required to assess potential impacts effectively. In this study, we treat sea level data as signal data, leveraging its temporal structure to apply advanced signal processing techniques. Specifically, we employ Seasonal- Trend Decomposition using Loess (STL) to isolate the underlying trend by removing seasonal components and noise. This extracted trend is then used to train predictive models. We propose a hybrid framework that integrates STL decomposition with deep learning architectures, focusing on CNN-LSTM and CNN-GRU networks with Physics Information to capture both spatial and temporal dependencies in sea level data.