Segun E. Ibitoye, Amit Kumar Ball, Raul V. Rodriguez, Olalekan Adebayo Olayemi, Peplluis Esteva, Peter Omoniyi, Isaac K. Adegun, Rasheedat M. Mahamood, Esther T. Akinlabi
ABSTRACT Global municipal solid waste generation is projected to exceed 3.8 billion tonnes annually by 2050. This makes the need for smart, inclusive, and scalable waste valorization systems more urgent than ever. This review critically explores the shift from conventional waste management to intelligent, technology‐driven solutions aligned with circular economy goals. Key findings highlight the transformative role of digital tools in waste classification, forecasting, and real‐time monitoring. Long Short‐Term Memory models achieved up to 94% accuracy in biogas prediction, while XGBoost demonstrated 98.5% accuracy in solid waste generation forecasting. Deep learning systems have reached classification accuracies of 83.11% across 28 recyclable categories and mean average precision scores up to 63% in complex waste detection. Despite promising advances, challenges such as data quality, regulatory hurdles, and system interoperability persist. This article contributes both a conceptual and practical blueprint for stakeholders, positioning smart waste valorization as a strategic opportunity to drive innovation.