Modeling of Sea Level Variation (SLV) is a complicated phenomenon owing to multiple factors that happen at different spatial and temporal scales. Thus, this paper presents an innovative multistep interdependent framework based on Wavelet Transformation (WT) and Artificial Intelligence (AI) algorithms for SLV prediction. A wavelet time-frequency approach along with harmonic analysis is performed firstly to understand deeply SLV behavior. Then, Neighborhood Component Analysis (NCA) is applied for the Feature selection (FS) purposes. Finally, a Deep Learning Neural Network (DLNN) algorithm is utilized to predict precisely SLV based on a newly compacted dataset. The findings revealed the potential of the DLNN model over the Machine Learning models as it improves the SLV prediction accuracy by 23%. Additionally, the proposed DLNN model can predict SLV for a time horizon of three days with a correlation coefficient = 0.91 that can help in predicting SLV early for disaster management purposes.