This article discusses the application of machine learning (ML) in membrane design, focusing on three main areas: (1) predicting membrane properties using ML, (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 like water filtration and gas separation. However, traditional experimental methods are time-consuming and inefficient, prompting the use of ML to accelerate discovery and design. ML models can rapidly screen and optimize membranes, reducing the need for extensive experimental work. For polymeric membranes, factors such as polymer type, solvent, and additives significantly affect performance, while for 2D membranes, nanopore geometry and material type are critical. ML models, trained on experimental and computational data, can predict properties like gas permeability, water flux, and ion rejection. Explainable AI methods, such as SHAP analysis, help interpret these predictions and identify key features influencing membrane performance. ML-assisted screening and optimization, including Bayesian optimization and deep reinforcement learning, enable efficient discovery of optimal membrane materials. Challenges include the lack of large, consistent datasets and the complexity of multi-objective optimization. Despite these challenges, ML is transforming membrane design, offering new opportunities for innovation in materials science and chemical engineering. The integration of ML with automated laboratory techniques holds promise for accelerating material discovery in various applications, including water purification and renewable energy.This article discusses the application of machine learning (ML) in membrane design, focusing on three main areas: (1) predicting membrane properties using ML, (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 like water filtration and gas separation. However, traditional experimental methods are time-consuming and inefficient, prompting the use of ML to accelerate discovery and design. ML models can rapidly screen and optimize membranes, reducing the need for extensive experimental work. For polymeric membranes, factors such as polymer type, solvent, and additives significantly affect performance, while for 2D membranes, nanopore geometry and material type are critical. ML models, trained on experimental and computational data, can predict properties like gas permeability, water flux, and ion rejection. Explainable AI methods, such as SHAP analysis, help interpret these predictions and identify key features influencing membrane performance. ML-assisted screening and optimization, including Bayesian optimization and deep reinforcement learning, enable efficient discovery of optimal membrane materials. Challenges include the lack of large, consistent datasets and the complexity of multi-objective optimization. Despite these challenges, ML is transforming membrane design, offering new opportunities for innovation in materials science and chemical engineering. The integration of ML with automated laboratory techniques holds promise for accelerating material discovery in various applications, including water purification and renewable energy.