Soil salinity is a grave environmental threat to agricultural development and food security in large parts of the world, especially in the situation of global warming and sea level rise. Reliable information on the adaptive capacity of farms plays a key role in reducing the socioeconomic effects of soil salinization and helps policymakers and farmers propose more appropriate measures to combat the phenomenon. The aim of the research is to design a theoretical framework to assess soil salinity and farmers' adaptive capacity, based on machine learning, optimization algorithms (namely Xgboost (XGB), XGB- Pelican Optimization Algorithm (POA), XGB- Siberian Tiger Optimization (STO), XGB- Serval Optimization Algorithm (SOA), XGB- Particle Swarm Optimization (PSO), and XGB- Grasshopper Optimization Algorithm (GOA)), remote sensing, and interviews with local people. The geographical distribution of soil salinity was evaluated by applying machine learning Sentinel 1 and 2A. The adaptive capacity of farmers was evaluated through interviews with 87 households. The statistical indices, namely the mean absolute error (MAE), the root mean square error (RMSE), and the correlation coefficient (R²) were used to assess the machine learning models. The outcome of this study demonstrated that all optimization algorithms were successful in improving the accuracy of the XGB model. The XGB-POA was the most performance, with an R<sup>2</sup> value of 0.968, followed by XGB-STO (R² = 0.967), XGB-SOA (R² = 0.966), XGB-PSO (R<sup>2</sup> = 0.964), and XGB-GOA (R² = 0.964), respectively. The soil salinity map produced by the proposed models also indicated that the coastal and riverside regions were the most affected by soil salinity. The results also showed human and financial resources to be the two most important factors influencing the adaptive capacity of farmers. This study offers a key theoretical framework that supplements the previous studies and can support policy-markers and farmers in land resource management, for example accurately identifying areas affected by soil salinity for agricultural development in the context of climate change. In addition, this research highlights the importance of integrating machine learning, remote sensing, and socio-economic surveys in soil salinity management, which can support farmers for sustainable agricultural development.