Sea level rise due to climate change poses an increasing threat to coastal ecosystems, infrastructure, and human settlements. However, accurately estimating sea level changes in regions without tide gauge observations remains a major challenge. While satellite altimetry provides wide spatial coverage, its accuracy diminishes near coastlines. In contrast, tide gauges offer high precision but are spatially limited. This study aims to develop an artificial neural network-based model for estimating relative sea level changes in coastal regions where tide gauge data are unavailable. Unlike conventional forecasting approaches focused on future time series prediction, the proposed model is designed to learn spatial distribution patterns and temporal rates of sea level change from a fusion of satellite altimetry and tide gauge data. A normalization scheme is applied to reduce inconsistencies in reference levels, and Bayesian optimization is employed to fine-tune hyperparameters. A case analysis is conducted in two coastal regions in South Korea, Busan and Ansan, using data from 2018 to 2023. The model demonstrates strong agreement with observed tide gauge records, particularly in estimating temporal trends of sea level rise. This approach effectively compensates for the limitations of satellite altimetry in coastal regions and fills critical observational gaps in ungauged areas. The proposed method holds substantial promise for coastal hazard mitigation, infrastructure planning, and climate adaptation strategies.