Marine Microbe-Based Plastic Degradation Predictor: An AI-Driven Bio-remediation Decision Support System.

Abstract

Plastic pollution has emerged as one of the most persistent and widespread environmental challenges affecting marine ecosystems globally. Conventional physical and chemical remediation approaches are often inefficient, costly, and environmentally intrusive, particularly for microplastics. Marine microorganisms capable of colonizing plastic surfaces—collectively termed the plastisphere—have demonstrated significant potential for plastic biodegradation through enzymatic and metabolic pathways. However, the identification and deployment of effective microbial consortia under diverse marine conditions remain poorly optimized due to the complexity of microbial–environment–polymer interactions. This study presents an AI-driven Marine Microbe-Based Plastic Degradation Predictor (MMPDP), a decision support system designed to predict the degradation potential of marine plastics based on microbial community profiles, environmental parameters, and polymer characteristics. Using curated global plastisphere datasets and machine learning models, the system predicts degradation efficiency and recommends suitable microbial taxa for targeted bioremediation. The proposed framework integrates microbiology, environmental science, and artificial intelligence, offering a scalable and data-driven approach to enhance marine plastic bioremediation strategies.