This article presents an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal, and mechanical properties. The workflow involves an automated pipetting robot preparing 286 nanocomposite films with varying compositions to train a support-vector machine (SVM) classifier. Through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated, establishing an artificial neural network (ANN) prediction model. The model can predict the physicochemical properties of nanocomposites from their compositions and automate the inverse design of biodegradable plastic substitutes that meet specific user requirements. The methodology integrates robot-assisted experiments, machine intelligence, and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes using generally-recognized-as-safe (GRAS) natural components. The approach demonstrates the ability to discover and fabricate a wide range of all-natural substitutes with analogous properties to non-biodegradable plastics, addressing the challenges of current biodegradable plastic development.This article presents an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal, and mechanical properties. The workflow involves an automated pipetting robot preparing 286 nanocomposite films with varying compositions to train a support-vector machine (SVM) classifier. Through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated, establishing an artificial neural network (ANN) prediction model. The model can predict the physicochemical properties of nanocomposites from their compositions and automate the inverse design of biodegradable plastic substitutes that meet specific user requirements. The methodology integrates robot-assisted experiments, machine intelligence, and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes using generally-recognized-as-safe (GRAS) natural components. The approach demonstrates the ability to discover and fabricate a wide range of all-natural substitutes with analogous properties to non-biodegradable plastics, addressing the challenges of current biodegradable plastic development.