This study presents an integrated workflow that combines collaborative robotics with machine learning to accelerate the design of conductive MXene aerogels with programmable properties. The workflow consists of four main phases: screening, analysis, design/optimization, and self-correction/validation. An automated pipetting robot prepares 264 mixtures of Ti3C2Tx MXene, cellulose, gelatin, and glutaraldehyde at different ratios and loadings. After freeze-drying, the structural integrity of the aerogels is evaluated to train a support vector machine (SVM) classifier, which defines a feasible parameter space. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated and characterized, enabling the construction of an artificial neural network (ANN) prediction model. The model can predict the aerogels' physicochemical properties from fabrication parameters and automate the inverse design of aerogels for specific property requirements. Model interpretation and finite element simulations validate the correlation between aerogel density and compressive strength. The model-suggested aerogels exhibit high conductivity, customized strength, and pressure insensitivity, making them suitable for wearable thermal management applications. The integrated approach not only accelerates the design process but also provides a versatile workflow for other nanoscience fields.This study presents an integrated workflow that combines collaborative robotics with machine learning to accelerate the design of conductive MXene aerogels with programmable properties. The workflow consists of four main phases: screening, analysis, design/optimization, and self-correction/validation. An automated pipetting robot prepares 264 mixtures of Ti3C2Tx MXene, cellulose, gelatin, and glutaraldehyde at different ratios and loadings. After freeze-drying, the structural integrity of the aerogels is evaluated to train a support vector machine (SVM) classifier, which defines a feasible parameter space. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated and characterized, enabling the construction of an artificial neural network (ANN) prediction model. The model can predict the aerogels' physicochemical properties from fabrication parameters and automate the inverse design of aerogels for specific property requirements. Model interpretation and finite element simulations validate the correlation between aerogel density and compressive strength. The model-suggested aerogels exhibit high conductivity, customized strength, and pressure insensitivity, making them suitable for wearable thermal management applications. The integrated approach not only accelerates the design process but also provides a versatile workflow for other nanoscience fields.