This study addresses the significant challenge posed by soil salinization in the fertile Nile Delta region, which threatens agricultural productivity and food security. Conventional methods for soil salinity assessment often lack the requisite speed for timely decision-making to mitigate salinity in these lands, necessitating the exploration of advanced techniques. Leveraging the capabilities of machine learning algorithms, this research develops robust predictive models for soil salinity in the Nile Delta. Three state-of-the-art machine learning algorithms: Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Random Forest (RF), were rigorously evaluated using a comprehensive dataset derived from 120 soil samples collected across the region. The models underwent meticulous training and validation processes, incorporating cross-validation techniques and stringent performance evaluation metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Squared Error (RMSE), and R2. The results unequivocally demonstrated the superior performance of SVM, achieving remarkable values of 0.006 dS/m for MSE, 0.079 dS/m for RMSE, 0.007 dS/m for MAPE, 0.062 dS/m for MAE and 1.0 for R2 during the training phase, further corroborated by an 0.008 dS/m for MSE, 0.089 dS/m for RMSE, 0.012 dS/m for MAPE, 0.071 dS/m for MAE and 0.99 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