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Effective waste management is critical for protecting human health and preserving environmental quality worldwide. Rapid urbanization and increasing waste generation have intensified the need for sustainable and efficient waste handling systems. 

In recent years, artificial intelligence (AI) has become a transformative technology, providing innovative solutions to improve waste collection, classification, routing, and disposal. These advancements contribute to greater resource recovery, lower operational costs, and reduced environmental impact. This study presents a systematic review of 50 scientific records published between 2014 and 2024, focusing on the waste management chain with a special concentration on AI applications. 

The review addresses a significant gap in the literature concerning AI-based waste management in port environments by thoroughly analyzing and classifying the waste management process into four key stages: classification, collection/vehicle routing, decision-making, and site selection. 

Our analysis reveals a diverse range of AI models employed across these stages, highlighting the superior accuracy and performance of many hybrid and advanced algorithms. 

The review further demonstrates the real-world optimization of waste management processes achieved through AI integration. Additionally, a comparative analysis between municipal and port waste management systems is conducted to explore the transferability and adaptation of successful AI approaches within port contexts. 

Building on these insights, the study proposes an AI-based framework for integrated port waste management, leveraging lessons learned from municipal systems to address the unique challenges of port environments. 

This framework aims to boost operational efficiency, sustainability, and resilience in port waste management.

Anass Hamraoui, Hamid Ech-cheikh, Abdessamad Douraid, Ahmed Loukili, Saad Lissane Elhaq, Mohammed Chaoui & Zouhair Boufakri.