This mini-review summarizes the application of machine learning (ML) in membrane design, focusing on three key areas: (1) predicting membrane properties, (2) using explainable AI (XAI) to understand the relationship between membrane features and performance, and (3) ML-guided design, optimization, or virtual screening of membranes. Porous membranes, including polymeric and two-dimensional (2D) materials, are crucial for applications such as water filtration and gas separation. However, traditional experimental methods are time-consuming and inefficient for discovering new membranes. ML models offer a data-driven approach to accelerate the design and optimization of membranes by enabling rapid screening and prediction of properties. In property prediction, ML models, such as neural networks and Gaussian Process Regression, have been used to predict gas permeability, salt rejection, and water flux in polymeric and 2D membranes. These models are trained on labeled datasets and can provide accurate predictions with high correlation factors. XAI methods, such as SHAP analysis, help interpret the relationships between membrane features and performance, providing insights into the physical mechanisms underlying membrane behavior. For membrane design and optimization, ML-assisted screening and optimization techniques, including Bayesian optimization and deep reinforcement learning, are used to identify optimal membrane materials and geometries. These methods enable efficient exploration of the vast search space of membrane materials and properties, leading to improved performance in applications such as water desalination and gas separation. Despite these advancements, challenges remain, including the lack of large, consistent datasets, the choice of featurization methods, and the multiobjective nature of membrane design. Future research should focus on addressing these challenges to further enhance the effectiveness of ML in membrane design. The integration of ML with automated laboratory techniques holds promise for accelerating material discovery and advancing applications in water purification, gas separation, biomedical devices, and renewable energy technologies.This mini-review summarizes the application of machine learning (ML) in membrane design, focusing on three key areas: (1) predicting membrane properties, (2) using explainable AI (XAI) to understand the relationship between membrane features and performance, and (3) ML-guided design, optimization, or virtual screening of membranes. Porous membranes, including polymeric and two-dimensional (2D) materials, are crucial for applications such as water filtration and gas separation. However, traditional experimental methods are time-consuming and inefficient for discovering new membranes. ML models offer a data-driven approach to accelerate the design and optimization of membranes by enabling rapid screening and prediction of properties. In property prediction, ML models, such as neural networks and Gaussian Process Regression, have been used to predict gas permeability, salt rejection, and water flux in polymeric and 2D membranes. These models are trained on labeled datasets and can provide accurate predictions with high correlation factors. XAI methods, such as SHAP analysis, help interpret the relationships between membrane features and performance, providing insights into the physical mechanisms underlying membrane behavior. For membrane design and optimization, ML-assisted screening and optimization techniques, including Bayesian optimization and deep reinforcement learning, are used to identify optimal membrane materials and geometries. These methods enable efficient exploration of the vast search space of membrane materials and properties, leading to improved performance in applications such as water desalination and gas separation. Despite these advancements, challenges remain, including the lack of large, consistent datasets, the choice of featurization methods, and the multiobjective nature of membrane design. Future research should focus on addressing these challenges to further enhance the effectiveness of ML in membrane design. The integration of ML with automated laboratory techniques holds promise for accelerating material discovery and advancing applications in water purification, gas separation, biomedical devices, and renewable energy technologies.