Groundwater salinization is considered as a major environmental problem in the coastal areas worldwide influencing ecosystems and human health. In Bangladesh, groundwater quality and quantity in the coastal regions have both significantly degraded as a result of saline water intrusion due to sea-level rise, storm surge flooding and excessive pumping of groundwater over the past few decades. Coastal aquifers are experiencing saltwater intrusion due to the hydraulic connection between groundwater and seawater. However, an accurate prediction of salinity concentration in groundwater remains a challenge due to the complexity of groundwater salinization processes and its influencing factors. In this study, machine learning (ML) algorithms has been performed for predicting groundwater salinity and identifying its influencing factors. This study was conducted in the southwestern coastal zones, i. e., Khulna, Bagerhat, and Satkhira of Bangladesh using geospatial database of 215 groundwater samples and different conditioning factors. Then, the predictive performances of different ML algorithms were compared, i.e., the Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and the Support Vector Machine. The model performance was assessed by using root-mean-square error (RMSE) and coefficient of determination (R²). The results show that the ANN model has the highest performance with R² = 0.92 and RMSE = 56.45 and six of the 10 influencing factors, including distance from shoreline, groundwater level, aquitard thickness, hydraulic conductivity, distance from the river, river salinity are the most important factors for groundwater salinity prediction. The results of the study will provide policy makers with useful information to tackle the issue of elevated salt levels in groundwater in coastal lowlands caused by excessive extraction. Human actions are a significant factor in the escalation of groundwater salinization, making it imperative to take prompt action based on the findings to promote sustainable management of groundwater in the southwestern coastal region.