To monitor groundwater salinization due to seawater intrusion (SWI) in the Nile Delta coastal aquifer, the present study developed a predictive regression model based on an innovative approach using SWI indicators and artificial intelligence (AI) methodologies. Hydrogeological and hydrogeochemical data of the groundwater wells in different periods were used as input data for the AI methods. The most significant inputs were determined, including the studied year, the distance from the shoreline, the aquifer type, and the hydraulic head. These inputs were used to build four basic AI models of logistic regression, Gaussian process regression, feedforward backpropagation neural networks (FFBPN), and deep learning-based long-short-term memory. The FFBPN model achieved higher evaluation results than other models in terms of root mean square error (RMSE) and R2 values in the testing phase, with R2 values of 0.9667, 0.9316, and 0.9259 for BEX, GQISWI, and water quality, respectively. Accordingly, the FFBPN was used to build a predictive model for electrical conductivity for the years 2020 and 2030. The results show that the 1000 μS/cm boundary is expected to move inland ~9.5 km (eastern part) to ~10 km (western part) to ~12.4 km (central part) between 2018 and 2030. This encroachment would be hazardous to water resources and agriculture unless action plans are taken.