Researchers at the Agricultural University of Athens have developed a machine learning system capable of rapidly predicting how quickly PHBV, a widely used biodegradable plastic, breaks down in natural environments. Led by Chrysanthos Maraveas and published in the journal Polymers, the study addresses a longstanding bottleneck in bioplastic research: traditional biodegradation testing requires months or years of laboratory observation. The team compiled a curated dataset from 13 peer-reviewed studies spanning nearly three decades, encompassing 93 experimental instances and over 1,300 individual measurements focused on carbon dioxide evolution as a marker for mineralization. Utilizing Random Forest and XGBoost algorithms, the researchers trained predictive models that achieved coefficients of determination between 0.95 and 0.97 on held-out test data, demonstrating robust generalization capabilities. Feature analysis confirmed that elapsed time remains the primary driver of degradation, while secondary but significant influences include ambient temperature, the ratio of hydroxyvalerate to hydroxybutyrate monomers, microbial community composition, additive formulations, and surface erosion mechanisms. These findings underscore the complex interplay between material engineering and environmental variables in determining bioplastic lifespan. The Random Forest model has been deployed as a free, interactive web application on the Jaqpot platform. Users can input specific polymer formulations and environmental parameters to receive immediate biodegradation forecasts. This capability supports a safe-and-sustainable-by-design framework for materials development, allowing manufacturers and researchers to optimize bioplastic compositions without lengthy trial-and-error cycles. PHBV, a bacterial biopolymer that does not generate persistent microplastics, holds particular promise for humanitarian and low-infrastructure regions where conventional waste management systems are unavailable. The open-access tool effectively accelerates the transition from fossil-based plastics to environmentally benign alternatives by providing rapid, data-driven insights into material breakdown kinetics.