This study presents an integrated workflow combining collaborative robotics and machine learning to accelerate the design of conductive MXene aerogels with programmable properties. The workflow involves an automated pipetting robot to prepare 264 mixtures of Ti3C2Tx MXene, cellulose, gelatin, and glutaraldehyde at different ratios and loadings. After freeze-drying, the aerogels' structural integrity is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated and characterized, enabling the construction of an artificial neural network prediction model. The model conducts two-way design tasks: (1) predicting the aerogels' physicochemical properties from fabrication parameters and (2) automating the inverse design of aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and compressive strength. The model-suggested aerogels with high conductivity, customized strength, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management. Conductive aerogels have gained significant research interest due to their ultralight characteristics, adjustable mechanical properties, and outstanding electrical performance. These attributes make them desirable for a range of applications, spanning from pressure sensors to electromagnetic interference shielding, thermal insulation, and wearable heaters. Conventional methods for the fabrication of conductive aerogels involve the preparation of aqueous mixtures of various building blocks, followed by a freeze-drying process. Key building blocks include conductive nanomaterials like carbon nanotubes, graphene, Ti3C2Tx MXene nanosheets, functional fillers like cellulose nanofibers, silk nanofibrils, and chitosan, polymeric binders like gelatin, and crosslinking agents that include glutaraldehyde (GA) and metal ions. By adjusting the proportions of these building blocks, one can fine-tune the end properties of the conductive aerogels, such as electrical conductivities and compression resilience. However, the correlations between compositions, structures, and properties within conductive aerogels are complex and remain largely unexplored. Therefore, to produce a conductive aerogel with user-designated mechanical and electrical properties, labor-intensive and iterative optimization experiments are often required to identify the optimal set of fabrication parameters. Creating a predictive model that can automatically recommend the ideal parameter set for a conductive aerogel with programmable properties would greatly expedite the development process. Machine learning (ML) is a subset of artificial intelligence (AI) that builds models for predictions or recommendations. AI/ML methodologies serve as an effective toolbox to unravel intricate correlations within the parameter space with multiple degrees of freedom. The AI/ML adoption in materials science research has surged, particularly in the fields with available simulation programs and high-throughput analytical tools that generate vast amounts of dataThis study presents an integrated workflow combining collaborative robotics and machine learning to accelerate the design of conductive MXene aerogels with programmable properties. The workflow involves an automated pipetting robot to prepare 264 mixtures of Ti3C2Tx MXene, cellulose, gelatin, and glutaraldehyde at different ratios and loadings. After freeze-drying, the aerogels' structural integrity is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated and characterized, enabling the construction of an artificial neural network prediction model. The model conducts two-way design tasks: (1) predicting the aerogels' physicochemical properties from fabrication parameters and (2) automating the inverse design of aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and compressive strength. The model-suggested aerogels with high conductivity, customized strength, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management. Conductive aerogels have gained significant research interest due to their ultralight characteristics, adjustable mechanical properties, and outstanding electrical performance. These attributes make them desirable for a range of applications, spanning from pressure sensors to electromagnetic interference shielding, thermal insulation, and wearable heaters. Conventional methods for the fabrication of conductive aerogels involve the preparation of aqueous mixtures of various building blocks, followed by a freeze-drying process. Key building blocks include conductive nanomaterials like carbon nanotubes, graphene, Ti3C2Tx MXene nanosheets, functional fillers like cellulose nanofibers, silk nanofibrils, and chitosan, polymeric binders like gelatin, and crosslinking agents that include glutaraldehyde (GA) and metal ions. By adjusting the proportions of these building blocks, one can fine-tune the end properties of the conductive aerogels, such as electrical conductivities and compression resilience. However, the correlations between compositions, structures, and properties within conductive aerogels are complex and remain largely unexplored. Therefore, to produce a conductive aerogel with user-designated mechanical and electrical properties, labor-intensive and iterative optimization experiments are often required to identify the optimal set of fabrication parameters. Creating a predictive model that can automatically recommend the ideal parameter set for a conductive aerogel with programmable properties would greatly expedite the development process. Machine learning (ML) is a subset of artificial intelligence (AI) that builds models for predictions or recommendations. AI/ML methodologies serve as an effective toolbox to unravel intricate correlations within the parameter space with multiple degrees of freedom. The AI/ML adoption in materials science research has surged, particularly in the fields with available simulation programs and high-throughput analytical tools that generate vast amounts of data