Plastic waste accumulation represents a persistent environmental challenge driven by rapidly increasing polymer production, short product lifecycles, and insufficient end-of-life management. Although the circular economy emphasizes recyclability and material value retention, real-world plastic waste streams are highly heterogeneous, contaminated, and economically constrained, limiting the effectiveness of single-pathway solutions. This review synthesizes recent advances and provides a systems-level assessment of scalable plastic waste management strategies under circular economy constraints, evaluating mechanical, chemical, thermochemical, and biological treatment routes in terms of material circularity, scalability, environmental trade-offs, and deployment limits. Reported studies indicate that artificial intelligence (AI)-assisted sorting systems can achieve classification accuracies exceeding 90–98%, while optimized hybrid recycling and logistics strategies can improve material recovery efficiencies by approximately 20–50% under controlled implementation conditions. Particular attention is given to the conditional role of microbial and enzymatic degradation, where engineered enzymatic and microbial systems have demonstrated 20–60% improvements in degradation performance compared with conventional baseline processes, although these pathways remain unsuitable as universal circular solutions due to kinetic, material, and infrastructural constraints. AI is examined as a cross-cutting enabler that supports waste sorting, process optimization, life-cycle assessment, and decision-making under uncertainty, with fuzzy logic and hybrid AI frameworks highlighted for their robustness in nonlinear and data-scarce waste systems. By synthesizing technological, economic, and policy barriers, the review clarifies key trade-offs between recyclability and degradability and positions energy recovery and biodegradation as complementary, residual pathways within a hierarchy-driven circular economy. Priority research and implementation opportunities are identified, including hybrid bio-thermochemical processing, AI-assisted system integration, standardized performance metrics, and coordinated policy instruments. Overall, this comprehensive review provides a coherent framework for environmentally grounded, data-informed, and scalable plastic waste management, supporting the transition from fragmented approaches toward integrated circular plastic systems. • Systems framework integrating AI, biodegradation, and circular plastics. • AI-enabled sorting improves recovery efficiency and recycling quality. • Biodegradation supports residual plastic treatment, not primary recycling. • Thermochemical routes recover carbon from non-recyclable plastics. • Policy, technology, and markets jointly drive circular plastic transitions.
Ali et al. (Thu,) studied this question.