This study addresses the pressing issue of soil salinization in the agriculturally vital Nile Delta region, which poses a significant threat to agricultural productivity and food security. Conventional methods for assessing soil salinity often lack the speed required for timely decision-making to effectively mitigate salinity in these lands, highlighting the need for advanced techniques. Harnessing the power of machine learning algorithms, this research endeavors to develop robust predictive models for soil salinity in the East Nile Delta (portsaid). Three state-of-the-art machine learning algorithms: Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Random Forest (RF), were rigorously applied using a comprehensive dataset derived from 60 soil samples collected across the region (portsaid government). The models underwent meticulous training and validation processes, incorporating cross-validation techniques and stringent performance evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R2. The results unequivocally demonstrated the superior performance of SVM, achieving remarkable values of 0.008 dS/m for MSE, 0.087 dS/m for RMSE, 0.009 dS/m for MAPE, 0.069 dS/m for MAE and 0.99 for R2 during the training phase, further corroborated by an 0.004 dS/m for MSE, 0.062 dS/m for RMSE, 0.006 dS/m for MAPE, 0.046 dS/m for MAE and 1 for R2 during the validation stage. This study elucidates the immense potential of machine learning techniques in accurately predicting soil salinity, paving the way for proactive management strategies and sustainable crop production practices in the pivotal Nile Delta region, thus enhancing sustainable crop production and agricultural management.