A machine intelligence-accelerated workflow combining robotics and machine learning has been developed to discover all-natural plastic substitutes with programmable optical, thermal, and mechanical properties. The approach uses four generally-recognized-as-safe (GRAS) natural components—cellulose nanofibers (CNFs), montmorillonite (MMT) nanosheets, gelatin, and glycerol—to fabricate nanocomposite films. An automated pipetting robot prepares 286 nanocomposite films with varying ratios of these components, which are then used to train a support-vector machine (SVM) classifier. Through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated, enabling the construction of an artificial neural network (ANN) model with high prediction accuracy. The model can predict the physicochemical properties of nanocomposites from their composition and automate the inverse design of biodegradable plastic substitutes that meet specific user requirements. The model's predictive power is used to prepare several all-natural substitutes that could replace non-biodegradable counterparts, exhibiting analogous properties. The methodology integrates robot-assisted experiments, machine intelligence, and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes from the GRAS database. The model was validated through experiments and molecular dynamics simulations, demonstrating high accuracy in predicting optical, fire-resistant, and mechanical properties. The model was further expanded to incorporate chitosan as a fifth building block, enhancing the range of achievable functions. The model's ability to predict multiple properties and automate the inverse design of biodegradable substitutes enables the development of a wide range of plastic replacements, including transparent badge holders, clear file folders, transparent shopping bags, translucent lamp shades, transparent air pillows, non-flammable battery packages, and UV-blocking chemical packages. The model's predictions were validated through biodegradability tests, showing that the all-natural substitutes decomposed completely after 5 weeks, while petrochemical plastics remained intact. The model's accuracy was further confirmed through sensitivity analyses and MD simulations, which revealed the strengthening mechanisms between CNF chains and MMT nanosheets. The study highlights the potential of AI/ML-integrated workflows in accelerating the discovery of eco-friendly plastic substitutes with programmable properties.A machine intelligence-accelerated workflow combining robotics and machine learning has been developed to discover all-natural plastic substitutes with programmable optical, thermal, and mechanical properties. The approach uses four generally-recognized-as-safe (GRAS) natural components—cellulose nanofibers (CNFs), montmorillonite (MMT) nanosheets, gelatin, and glycerol—to fabricate nanocomposite films. An automated pipetting robot prepares 286 nanocomposite films with varying ratios of these components, which are then used to train a support-vector machine (SVM) classifier. Through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated, enabling the construction of an artificial neural network (ANN) model with high prediction accuracy. The model can predict the physicochemical properties of nanocomposites from their composition and automate the inverse design of biodegradable plastic substitutes that meet specific user requirements. The model's predictive power is used to prepare several all-natural substitutes that could replace non-biodegradable counterparts, exhibiting analogous properties. The methodology integrates robot-assisted experiments, machine intelligence, and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes from the GRAS database. The model was validated through experiments and molecular dynamics simulations, demonstrating high accuracy in predicting optical, fire-resistant, and mechanical properties. The model was further expanded to incorporate chitosan as a fifth building block, enhancing the range of achievable functions. The model's ability to predict multiple properties and automate the inverse design of biodegradable substitutes enables the development of a wide range of plastic replacements, including transparent badge holders, clear file folders, transparent shopping bags, translucent lamp shades, transparent air pillows, non-flammable battery packages, and UV-blocking chemical packages. The model's predictions were validated through biodegradability tests, showing that the all-natural substitutes decomposed completely after 5 weeks, while petrochemical plastics remained intact. The model's accuracy was further confirmed through sensitivity analyses and MD simulations, which revealed the strengthening mechanisms between CNF chains and MMT nanosheets. The study highlights the potential of AI/ML-integrated workflows in accelerating the discovery of eco-friendly plastic substitutes with programmable properties.