Waste management systems face increasing environmental and budget constraints and must therefore deliver circular outcomes under limited resources. This systematic review, with a prospective lens, explores the intersection of frugal innovation, artificial intelligence, and the circular economy. It examines how low-compute artificial intelligence pipelines, deployed at the edge with minimal instrumentation, can enhance circular performance in real operational contexts. A PRISMA-aligned systematic review covering 2018 to mid-2025 was conducted across Scopus and Web of Science. The protocol involved double independent screening and a structured codebook. The synthesis identifies four main application families: forecasting and capacity planning, smart collection, vision-guided sorting with inline quality control, and circular dashboards integrated with digital twins. Reported outcomes show significant efficiency gains. In retail, machine-learning-based dynamic pricing cuts food waste by several tens of percent. An integrative framework is proposed to link frugal artificial intelligence mechanisms with micro-level indicators. These indicators include recovery, purity, contamination, diversion from landfill, cost per ton, material revenues, energy and carbon intensity, and compliance. Effective deployment depends on data quality, edge-level model quantization and pruning, threshold tuning, and operator appropriation. Although methods differ across studies, the use of standardized indicators and before–after quasi-experimental designs improves comparability and strengthens the credibility of the evidence. Overall, in waste management, circular value is increasingly driven by frugal innovation and artificial intelligence.