Soil salinity is a significant issue that threatens agricultural productivity and ecosystem balance worldwide. Analyzing soil salinity using traditional methods is a challenging, costly, and time-consuming process, as it relies on laboratory measurements, making it difficult to apply over large areas. Thus, there has been a rise in recent years in the trend toward utilizing remote sensing and machine learning methods to evaluate soil salinity quickly and effectively. Remote sensing can detect signs of salinity on the soil surface through spectral data obtained from satellites and aerial vehicles. Data from visible, near-infrared, and thermal bands, in particular, are widely used in mapping soil salinity. Machine learning algorithms process this spectral data to model complex relationships related to soil salinity and make predictions. Methods like Deep Learning, Artificial Neural Networks, Random Forests, and Support Vector Machines have attracted attention in this field due to their high accuracy rates. These methods hold great potential for monitoring changes in soil salinity, optimizing agricultural practices, and developing strategies to combat salinity. At the same time, they contribute to the advancement of sustainable agriculture by supporting soil management decisions, especially in large agricultural areas. In this context, the combination of machine learning and remote sensing technologies stands out as an effective solution for monitoring and managing soil salinity. Therefore, this research attempts to investigate the advantages and limitations of research conducted in this field and to provide a framework that can guide future studies.