Rapid urbanisation and rising municipal solid waste generation have made intelligent waste segregation an essential requirement for modern smart cities. Conventional classification techniques depend on manual sorting and are limited by cost, inconsistency and poor adaptability to complex environmental conditions. Deep learning has produced substantial improvements in automated waste recognition, but existing approaches frequently rely on single-model architectures, narrow datasets, or computationally expensive frameworks that restrict real-time deployment. This paper presents a comprehensive review of hybrid deep learning approaches for intelligent waste classification and smart waste management. Thirty-one representative studies published between 2016 and 2026 are organised into a four-generation taxonomy spanning classical convolutional networks, transfer and ensemble learning, vision transformers and hybrid architectures, and detection-oriented and deployable systems. The benchmark datasets used by the field are compared, the principal backbone architectures are contrasted by depth, parameter count and characteristic behaviour, and reported performance is analysed across the surveyed systems. The review shows that reported accuracy on standard corpora has risen from the low nineties for early convolutional systems to approximately ninety-nine per cent for recent fused convolutional-transformer designs, but that this progress is measured almost entirely on curated laboratory imagery. Five persistent gaps are identified — the absence of global context in single-CNN designs, limited dataset diversity and class imbalance, the separate treatment of classification and detection, computational cost that precludes edge deployment, and the absence of explainability — and each is mapped to a corresponding component of a proposed five-layer hybrid framework that integrates convolutional and transformer feature extraction, attention-based fusion, unified recognition and detection, and an explainable decision layer for smart city waste management.

Sapna Pandey, Neelesh Shrivastava,"Analysis and Comparative Review of CNN and Vision Transformer Based Models for Intelligent Waste Management" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(5): Page(406-412) September - October 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.