Soil suction is a crucial factor affecting the hydraulic and mechanical property of unsaturated soils, playing an important role in geotechnical, geoenvironmental, and hydrological engineering applications such as slope stability, foundation design and irrigation planning. Conventionally, measuring and modeling soil suction and its associated curves like Soil Water Characteristic Curve (SWCC) and Soil Water Retention Curve (SWRC) require extensive, time-consuming tests in the laboratory. Recent progress in Machine Learning (ML) offers powerful as well as data-driven and reliable alternatives ways that can enhance the efficiency and accuracy of suction-related predictions across a wide range of soil conditions. This study aims to cover the current state of the art research on the integration of ML techniques into the prediction and analysis of soil suction behavior. Studies utilized various algorithms including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Multi-Expression Programming (MEP), K-Nearest Neighbors (KNN), and AdaBoost (AB) to predict soil suction. These models demonstrated high predictive performance (R² > 0.90 in majority cases) based on soil parameters which can be easily evaluated like soil texture, bulk density, climate parameters, and remotely sensed data. Overall, this study covers the understanding of the current research gap related to SWCC and SWRC using different data driven and ML techniques.