We used a machine learning framework for the projection of soil salinity in the southwest of Bangladesh. Machine learning (ML) is a cutting-edge, data-driven way to understand and predict how soil salinity changes in coastal areas. We applied an explainable AI technique to understand the influences of the different physical factors on salinity dynamics in the study region. By analyzing large datasets that include climate variables (such as rainfall, temperature, and humidity), hydrological factors (like groundwater depth and river proximity), and land and environment characteristics (such as elevation and land use), ML models can identify complex relationships that traditional statistical models often miss. We applied different machine learning algorithms to learn patterns from past observations of soil salinity and use them to predict future conditions under different climate change scenarios. These predictions help researchers, farmers, and policymakers visualize where and when salinity levels may increase, supporting better planning for agriculture, promoting different salt-tolerant varieties, and climate adaptation. Ultimately, ML-based salinity projection enhances our ability to protect soil health, ensure food security, and build agriculture and environmental resilience in vulnerable coastal ecosystems.